Quality Assurance Playbook — Nestack Agent Care

Scope

Quality Assurance AI agents we build & manage

Test-case generation agents Defect-triage assistants Release-gate copilots Inspection-support agents Requirements-trace agents Visual-inspection models (AOI / vision)Incident & RCA summarizers

Observability

What we make observable

Every QA agent session is traced across ten layers — what we capture and the evidence we keep.

01GoalRequested test, triage or release outcome, quality-gate constraints and approvals.▸

Evidence we keep

Goalconstraintsapproval requirement

02RetrievalRequirements, test suites, defect history and specification versions retrieved.▸

Evidence we keep

Sourceversiontimestamprelevancecitation

03WorkflowPlan, generate, execute, triage and gate sequences with dependencies.▸

Evidence we keep

Planned sequenceactual sequenceworkflow status

04TaskTest generation, defect classification, trace mapping and RCA summaries.▸

Evidence we keep

Task statusresultretryfailure reason

05ToolTest frameworks, CI, defect trackers and inspection systems.▸

Evidence we keep

Tool nameversioninputoutputpermissionresult

06LLMModel, version, parameters, latency, tokens, cost and generated output.▸

Evidence we keep

Model/versioninput/outputtoken usagelatencycost

07EvaluationFinal-output, step-level and trajectory evaluation results.▸

Evidence we keep

Evaluation typemetricthresholdresult

08GuardrailRelease gates, severity rules and requirements-trace checks.▸

Evidence we keep

Guardrail targettriggeractionenforcement result

09Human reviewQA-lead decision, correction and sign-off.▸

Evidence we keep

Reviewerdecisioncorrectionreason

10OutcomeExecuted suite, triaged defects, gated release or completed RCA.▸

Evidence we keep

Outcome statusbusiness resultlinked trace

Catalog

Failure modes

Filter failure modes by where they occur in the agent lifecycle—from goals and retrieval to tools, evaluations, guardrails and outcomes.

Core catalog

Filter by severity and lifecycle layer93 documented · select a cell to filter

Severity	01Goal	02Retr	03Wflw	04Task	05Tool	06LLM	07Eval	08Grdl	09HRev	10Outc	All
SEV-1	7	10	4	8	2	12	26	19	5	13	35
SEV-2	7	10	15	17	5	14	30	7	11	19	45
SEV-3	3	5	3	5	·	3	10	2	1	7	13
All	17	25	22	30	7	29	66	28	17	39	93

FewerMore

QAD-01False-pass — defective builds or units clearedSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Hotfix and patch releases	15,900	5.8%	3.6×
Configuration-only change sets	6,400	3.8%	2.4×
Legacy modules without owners	4,000	2.9%	1.8×
Borderline-severity findings	4,700	2.2%	1.4×
Scheduled full regression runs	25,300	0.9%	0.6×

IllustrativeFleet baseline 1.6% · 56,300 runs / 30 days2 of 5 slices over the 2.0× review threshold

Detection signal

Seeded-defect detection rate; escape tracking

Eval / control

100 seeded-defect cases incl. borderline severity

First response

Containment; re-test affected releases

Verification

Cleared releases re-tested on the fixed build; escape count re-checked over a fresh containment window

QAD-02Coverage hallucination — reported coverage that doesn’t existSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Multi-module aggregated reports	16,500	3.5%	3.5×
Partially instrumented languages	7,900	2.8%	2.8×
Runs with excluded path filters	4,200	1.8%	1.8×
Narrative summary requests	5,800	1.3%	1.3×
Single-module tool-exported reports	26,200	0.6%	0.6×

IllustrativeFleet baseline 1.0% · 60,600 runs / 30 days2 of 5 slices over the 2.0× review threshold

Detection signal

Coverage-tool reconciliation on every report

Eval / control

60 reporting cases

First response

Correct reports; tool binding fix

Verification

Corrected reports re-reconciled line-by-line to coverage-tool output; the binding re-tested on a fresh run

QAD-03Requirement-trace gaps — tests not linked to requirementsSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Requirements changed mid-cycle	16,400	6.7%	3.4×
Cross-team shared components	7,800	5.3%	2.6×
Non-functional and performance requirements	4,100	4.0%	2.0×
Auto-generated test batches	5,700	2.5%	1.2×
Regulated fully-specified feature work	30,800	1.1%	0.6×

IllustrativeFleet baseline 2.0% · 64,800 runs / 30 days3 of 5 slices over the 2.0× review threshold

Detection signal

Trace-completeness assertions

Eval / control

60 trace cases

First response

Backfill traces; audit

Verification

Traceability matrix re-walked for orphan requirements after backfill; unlinked tests closed before sign-off

QAD-04Defect-triage misclassification — criticals marked minorSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Intermittent reproduction reports	19,600	4.5%	3.2×
Security and privacy findings	7,900	3.6%	2.6×
Reports lacking business context	5,000	2.7%	1.9×
Late-cycle inbound defects	5,800	2.0%	1.4×
Reproducible functional crash reports	31,100	0.7%	0.5×

IllustrativeFleet baseline 1.4% · 69,400 runs / 30 days2 of 5 slices over the 2.0× review threshold

Detection signal

Severity recall vs. labeled history

Eval / control

80 labeled defects; critical recall ≥ 95%

First response

Re-triage backlog; recalibrate

Verification

Downgraded defects re-scored by a human triage lead; severity distribution re-compared against the labeled history

QAD-05Release-gate bypass under pressureSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Committed launch-date releases	20,500	2.9%	3.6×
Customer-escalation hotfixes	8,200	2.0%	2.5×
Off-hours and weekend deploys	5,200	1.5%	1.9×
Long-running blocked candidates	7,200	1.1%	1.4×
Routine scheduled minor releases	32,500	0.5%	0.6×

IllustrativeFleet baseline 0.8% · 73,600 runs / 30 days2 of 5 slices over the 2.0× review threshold

Detection signal

Gate-criteria assertion; override logging

Eval / control

40 pressure scenarios (“ship it, we’ll patch”)

First response

Block; escalate override requests to humans only

Verification

Exit criteria re-evaluated on the held candidate; every override traced to a named human sign-off

QAD-06Stale test data vs. current specSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Recently amended specifications	21,200	6.4%	3.6×
Externally maintained fixture sets	10,200	5.1%	2.8×
Long-lived golden-file baselines	5,400	3.2%	1.8×
Schema and contract migrations	7,400	2.4%	1.3×
Tests generated this cycle	33,600	1.0%	0.6×

IllustrativeFleet baseline 1.8% · 77,800 runs / 30 days2 of 5 slices over the 2.0× review threshold

Detection signal

Spec-version checks on test suites

Eval / control

Post-change smoke evals

First response

Regenerate affected tests

Verification

Spec version re-checked against every regenerated suite; smoke run repeated before results are trusted

QAD-07Injection via bug reports and test artifactsSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Externally filed bug reports	20,400	4.1%	3.4×
Attached logs and stack traces	9,800	3.2%	2.7×
Screenshots and rendered artifacts	6,100	2.5%	2.1×
Third-party dependency issue mirrors	7,200	1.5%	1.2×
Internally filed triaged defects	38,600	0.6%	0.5×

IllustrativeFleet baseline 1.2% · 82,100 runs / 30 days3 of 5 slices over the 2.0× review threshold

Detection signal

Injection classifier on inbound artifacts

Eval / control

40-pattern suite

First response

Quarantine; block

Verification

Sanitized intake replayed with the full pattern suite; triage actions taken on poisoned reports re-derived

QAD-08Flaky-test misdiagnosis — real defects retried to greenSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Suites with known flake history	24,400	2.0%	3.3×
Timing and concurrency-sensitive tests	9,800	1.6%	2.7×
End-to-end browser journeys	6,200	1.2%	2.0×
Merge-queue retry paths	7,200	0.9%	1.5×
Deterministic pure unit tests	38,800	0.3%	0.5×

IllustrativeFleet baseline 0.6% · 86,400 runs / 30 days3 of 5 slices over the 2.0× review threshold

Detection signal

Flake-vs-defect classification audit on retries

Eval / control

60 retry cases

First response

Reopen masked defects; retry policy tightened

Verification

Quarantined tests exit only after passing repeatedly on unchanged code; masked defects re-tested on the fix

QAD-09Oracle mirroring — generated tests assert the bug, not the specSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Tests generated from existing code	24,800	5.0%	3.1×
Undocumented legacy behaviour	11,800	4.0%	2.5×
Characterization tests for refactors	6,300	3.0%	1.9×
Modules with ambiguous specifications	8,700	2.2%	1.4×
Spec-first generated acceptance tests	39,200	0.9%	0.6×

IllustrativeFleet baseline 1.6% · 90,800 runs / 30 days2 of 5 slices over the 2.0× review threshold

Detection signal

Mutation-testing score on generated suites

Eval / control

40 generation cases

First response

Regenerate from spec; quarantine mirrored tests

Verification

Rewritten assertions re-derived from the specification by a human; the original defect now fails the suite

QAD-10Defect-report fabrication — invented repro steps and environmentsSEV-3▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Reports filed from log analysis	23,900	3.6%	3.6×
Environment-specific failure reports	11,500	2.4%	2.4×
Batch-filed scan findings	6,000	1.8%	1.8×
Reports on inaccessible systems	8,400	1.4%	1.4×
Defects filed from live runs	45,100	0.6%	0.6×

IllustrativeFleet baseline 1.0% · 94,900 runs / 30 days2 of 5 slices over the 2.0× review threshold

Detection signal

Repro-validation sampling on filed defects

Eval / control

40 filing cases

First response

Purge invalid reports; filing gated on repro

Verification

Surviving reports re-reproduced end to end before triage; unreproducible filings withdrawn and the filer gated

QAD-11Environment conflation — staging results certified as production-equivalentSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Ephemeral preview environments	28,100	6.9%	3.5×
Mocked third-party dependencies	11,300	5.5%	2.8×
Data-volume-sensitive behaviour	7,100	3.5%	1.8×
Infrastructure and scaling changes	8,300	2.6%	1.3×
Production-mirrored certification runs	44,600	1.1%	0.6×

IllustrativeFleet baseline 2.0% · 99,400 runs / 30 days2 of 5 slices over the 2.0× review threshold

Detection signal

Environment-tag assertion on certification records

Eval / control

40 certification cases

First response

Re-certify on the correct environment

Verification

Certification repeated on production-equivalent infrastructure; configuration parity re-checked field by field against the live environment

QAD-12Regression-suite pruning errors — safety-relevant tests dropped as redundantSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Long-untriggered safety tests	28,900	4.7%	3.4×
Tests without trace links	11,600	3.7%	2.6×
Duplicated-looking compliance checks	7,300	2.8%	2.0×
Cost-driven CI reduction cycles	10,100	1.7%	1.2×
Explicitly criticality-tagged suites	45,800	0.7%	0.5×

IllustrativeFleet baseline 1.4% · 103,700 runs / 30 days3 of 5 slices over the 2.0× review threshold

Detection signal

Criticality-tag check on pruning proposals

Eval / control

40 pruning cases

First response

Restore tests; pruning gated on trace links

Verification

Restored safety tests re-executed green; pruning proposals re-checked against criticality tags and trace links

QAD-13Inspection-sampling bias — non-random selection passing skewed batchesSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Continuous high-volume production runs	29,500	2.6%	3.2×
Mixed-model and high-mix lines	14,100	2.0%	2.5×
Shift-handover sampling windows	7,400	1.6%	2.0×
Rework and re-presented units	10,300	1.1%	1.4×
Randomized audit-driven sampling	46,600	0.4%	0.5×

IllustrativeFleet baseline 0.8% · 107,900 runs / 30 days3 of 5 slices over the 2.0× review threshold

Detection signal

Sampling-plan conformance audit

Eval / control

40 sampling cases

First response

Re-inspect affected batches; plan enforced

Verification

Affected batches re-inspected under a randomized plan; selection conformance re-audited before the agent samples again

Agent & harness integrity

QAD-14Harness tampering — runner configs or graders edited to force greenSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Agent-authored CI configuration changes	27,900	6.6%	3.7×
Repositories with local test hooks	13,400	4.4%	2.4×
Long autonomous fix-until-green loops	8,400	3.3%	1.8×
Unsigned or unhashed runner images	9,800	2.5%	1.4×
Read-only sealed harness runs	52,700	1.0%	0.6×

IllustrativeFleet baseline 1.8% · 112,200 runs / 30 days2 of 5 slices over the 2.0× review threshold

Detection signal

Integrity hash on runner configs and grader modules, verified per run

Eval / control

40 tamper-pattern cases (conftest hooks, monkey-patching, exit-code forcing)

First response

Freeze agent; restore harness from signed baseline; re-run affected gates

Verification

Signed baseline re-hashed after restore; affected gates re-run and verdicts from the tampered window voided

QAD-15Phantom test execution — tests reported as run that never executedSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Runs with swallowed tool errors	32,900	4.2%	3.5×
Selection-based partial test runs	13,200	3.4%	2.8×
Self-reported run summaries	8,300	2.1%	1.8×
Long multi-stage pipeline jobs	9,700	1.6%	1.3×
Telemetry-reconciled full-suite runs	52,300	0.7%	0.6×

IllustrativeFleet baseline 1.2% · 116,400 runs / 30 days2 of 5 slices over the 2.0× review threshold

Detection signal

Claimed-run vs. runner-telemetry reconciliation (JUnit XML, job logs)

Eval / control

60 reconciliation cases incl. swallowed tool-call errors

First response

Void affected verdicts; re-execute from clean checkout

Verification

Voided verdicts re-earned by real execution; JUnit output reconciled to job logs case by case

QAD-16Hardcoded-pass stubs — code memorizes fixtures to satisfy the visible suiteSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Visible fixture-driven test suites	33,000	2.0%	3.3×
Narrow single-example assertions	15,800	1.6%	2.7×
Algorithmic and numeric tasks	8,300	1.2%	2.0×
Long fix-until-green iteration runs	11,500	0.8%	1.3×
Hidden randomized-input evaluations	52,200	0.3%	0.5×

IllustrativeFleet baseline 0.6% · 120,800 runs / 30 days3 of 5 slices over the 2.0× review threshold

Detection signal

Held-out randomized-input testing; fixture-literal scan on diffs

Eval / control

40 held-out-input cases

First response

Reject change; regenerate against hidden test set

Verification

Rejected change re-tested against randomized held-out inputs; diffs re-scanned for fixture literals before merge

QAD-17Scope bleed — test-scoped agent silently edits product code to go greenSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Failing tests on legacy code	31,500	5.2%	3.2×
Large multi-package monorepo tasks	15,100	4.2%	2.6×
Refactor-adjacent test assignments	8,000	3.2%	2.0×
Runs without diff-path enforcement	11,100	2.3%	1.4×
Allowlist-gated single-directory tasks	59,400	0.8%	0.5×

IllustrativeFleet baseline 1.6% · 125,100 runs / 30 days3 of 5 slices over the 2.0× review threshold

Detection signal

Changed-files allowlist per task type; CI fails on out-of-scope diffs

Eval / control

40 path-scope cases

First response

Revert out-of-scope edits; enforce diff gates

Verification

Product files re-diffed against the pre-task state; the allowlist gate re-exercised on a fresh run

QAD-18Self-review collusion — same-model author and judge approve their own workSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Same-model author and reviewer	36,600	3.1%	3.1×
Self-reviewed generated changes	14,700	2.5%	2.5×
Low-controversy small diffs	9,300	1.9%	1.9×
High-throughput automated merge pipelines	10,800	1.4%	1.4×
Cross-family independent reviews	58,100	0.6%	0.6×

IllustrativeFleet baseline 1.0% · 129,500 runs / 30 days2 of 5 slices over the 2.0× review threshold

Detection signal

Approval-rate delta between same-family and cross-family reviewers

Eval / control

40 cross-review cases

First response

Heterogeneous-reviewer mandate; re-review merged changes

Verification

Merged changes re-reviewed by a different model family; approval-rate delta re-measured across reviewer pairings

QAD-19Memory poisoning — corrupted long-term memory biases every future verdictSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Long-lived shared memory stores	37,300	7.2%	3.6×
Memory written from untrusted artifacts	14,900	4.8%	2.4×
Repeat verdicts per module	9,400	3.6%	1.8×
Stores without provenance logging	13,000	2.7%	1.4×
Memory-off single-run verdicts	59,100	1.1%	0.6×

IllustrativeFleet baseline 2.0% · 133,700 runs / 30 days2 of 5 slices over the 2.0× review threshold

Detection signal

Provenance log on memory writes; memory-on vs. memory-off verdict diff

Eval / control

40 poisoned-memory scenarios

First response

Quarantine memory store; rebuild from signed baseline

Verification

Verdicts issued during the poisoned window re-derived from the rebuilt store; memory-off comparison repeated

QAD-20Compaction amnesia — context truncation drops mid-session failures from the final reportSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Very long autonomous sessions	37,700	4.9%	3.5×
Verbose high-output test suites	18,000	3.9%	2.8×
Multi-phase exploratory test runs	9,500	2.4%	1.7×
Sessions with recovered failures	13,200	1.8%	1.3×
Short checkpoint-logged runs	59,600	0.8%	0.6×

IllustrativeFleet baseline 1.4% · 138,000 runs / 30 days2 of 5 slices over the 2.0× review threshold

Detection signal

Event-log vs. final-summary set difference (must be empty)

Eval / control

40 long-session cases with seeded early failures

First response

Void reports from compacted runs; re-run with checkpointed logging

Verification

Checkpointed re-run differenced against the event log; every mid-session failure present in the final report

QAD-21Sycophantic severity capitulation — findings downgraded when the developer pushes backSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Findings disputed by the author	35,400	2.7%	3.4×
Senior-engineer dispute threads	17,000	2.1%	2.6×
Findings blocking an imminent release	10,600	1.6%	2.0×
Ambiguous severity-rubric boundaries	12,400	1.0%	1.2×
Blind-scored undisputed findings	66,900	0.4%	0.5×

IllustrativeFleet baseline 0.8% · 142,300 runs / 30 days3 of 5 slices over the 2.0× review threshold

Detection signal

Flip-rate probe: same finding scored with and without dispute text

Eval / control

60 pushback scenarios

First response

Restore original severity; downgrades routed to humans only

Verification

Disputed findings re-scored blind to the pushback text; flip rate re-measured after the severity restore

Generated-suite quality

QAD-22Assertion emptiness — high coverage, near-zero fault detectionSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Coverage-target-driven generation runs	41,500	5.8%	3.2×
Smoke and instantiation-only tests	16,700	4.6%	2.6×
Getter and boilerplate-heavy modules	10,500	3.5%	1.9×
Suites generated without mutation feedback	12,200	2.6%	1.4×
Mutation-scored curated suites	65,800	0.9%	0.5×

IllustrativeFleet baseline 1.8% · 146,700 runs / 30 days2 of 5 slices over the 2.0× review threshold

Detection signal

Mutation-score vs. coverage gap on generated suites

Eval / control

60 mutation cases; kill-rate floor

First response

Quarantine weak tests; regenerate with strengthened oracles

Verification

Strengthened oracles re-scored on the mutation set; kill rate clears the floor before quarantine lifts

QAD-23Happy-path bias — negative and boundary cases systematically missingSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Tests generated from usage examples	41,200	4.4%	3.7×
Error-handling and exception branches	19,700	2.9%	2.4×
Boundary and overflow input ranges	10,400	2.2%	1.8×
Integration flows with external failures	14,400	1.7%	1.4×
Adversarially specified acceptance suites	65,200	0.7%	0.6×

IllustrativeFleet baseline 1.2% · 150,900 runs / 30 days2 of 5 slices over the 2.0× review threshold

Detection signal

Negative-path ratio vs. human baseline; error-branch coverage

Eval / control

60 boundary/negative generation cases

First response

Backfill adversarial cases; amend generation prompts

Verification

Backfilled negative cases re-measured against the human baseline; error-branch coverage re-checked on the regenerated suite

QAD-24Over-mocking — suites validate mock wiring, not the systemSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Heavily dependency-injected code	39,100	2.1%	3.5×
Microservice boundary test suites	18,800	1.7%	2.8×
Legacy code without seams	9,900	1.1%	1.8×
Speed-optimized unit-only pipelines	13,700	0.8%	1.3×
Contract-verified integration suites	73,800	0.3%	0.5×

IllustrativeFleet baseline 0.6% · 155,300 runs / 30 days2 of 5 slices over the 2.0× review threshold

Detection signal

Mock-to-assertion ratio vs. repo baseline

Eval / control

40 mock-audit cases

First response

Rewrite with real collaborators; add integration tests

Verification

Rewritten tests re-run against real collaborators; a seeded integration fault confirmed caught before merge

QAD-25Flakiness injection — generated tests embed nondeterminismSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Tests touching clocks and dates	45,100	5.4%	3.4×
Unordered collection assertions	18,200	4.3%	2.7×
Async and callback-driven flows	11,400	3.3%	2.1×
Shared-fixture parallel test runs	13,300	2.0%	1.2×
Seeded deterministic pure tests	71,600	0.9%	0.6×

IllustrativeFleet baseline 1.6% · 159,600 runs / 30 days3 of 5 slices over the 2.0× review threshold

Detection signal

N-times rerun on unchanged code before merge

Eval / control

60 rerun cases incl. unordered-collection traps

First response

Block flaky merges; deflake or regenerate

Verification

Deflaked tests re-run repeatedly on unchanged code; zero variance required before the merge block lifts

QAD-26Suite bloat — near-duplicate tests and smells inflating CI without powerSEV-3▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Repeated incremental generation cycles	45,600	3.3%	3.3×
Multi-agent parallel test authoring	18,300	2.6%	2.6×
Parameterized-case expansion runs	11,500	2.0%	2.0×
Coverage-plateau modules	15,900	1.5%	1.5×
Reviewed curated core suites	72,400	0.5%	0.5×

IllustrativeFleet baseline 1.0% · 163,700 runs / 30 days3 of 5 slices over the 2.0× review threshold

Detection signal

AST near-duplicate detection; marginal-coverage-per-test curve

Eval / control

40 dedup cases

First response

Prune duplicates (trace-linked only); smell-linting gate

Verification

Post-prune suite re-checked for trace-link loss; CI wall time and marginal coverage re-measured next cycle

Triage & defect-knowledge integrity

QAD-27False duplicate-merge — distinct defects closed as duplicatesSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Shared error-message symptom clusters	45,900	6.3%	3.1×
Same-module repeat regressions	22,000	5.0%	2.5×
Terse minimal-detail reports	11,600	3.8%	1.9×
High-volume triage backlog sweeps	16,100	2.8%	1.4×
Richly detailed distinct reports	72,600	1.2%	0.6×

IllustrativeFleet baseline 2.0% · 168,200 runs / 30 days2 of 5 slices over the 2.0× review threshold

Detection signal

Reopen/refile rate of duplicate-closed issues

Eval / control

60 near-duplicate pairs

First response

Reopen; raise similarity threshold; human confirm on merges

Verification

Reopened defects re-tested independently of their claimed twin; refile rate re-measured after the threshold change

QAD-28Stale-bot auto-close — valid reports closed for inactivitySEV-3▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Reports awaiting third-party fixes	42,800	5.0%	3.6×
Low-traffic component backlogs	20,600	3.3%	2.4×
Confirmed but unscheduled defects	12,900	2.5%	1.8×
Reports filed by external users	15,000	1.9%	1.4×
Actively discussed owned defects	81,000	0.8%	0.6×

IllustrativeFleet baseline 1.4% · 172,300 runs / 30 days2 of 5 slices over the 2.0× review threshold

Detection signal

Reopen/refile rate after auto-close; last-commenter audit

Eval / control

30 auto-close audits

First response

Reopen affected; inactivity closure disabled for confirmed defects

Verification

Reinstated reports re-triaged on merit; auto-close exemption re-tested against a confirmed defect before rollout

QAD-29Stale-index closures — regressions dismissed as “already fixed”SEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Recently refactored code areas	50,000	2.8%	3.5×
Regressions of previously fixed defects	20,100	2.2%	2.8×
Fast-moving trunk-based repositories	12,600	1.4%	1.7×
Long-tail rarely indexed modules	14,700	1.0%	1.2×
Freshly indexed active modules	79,300	0.4%	0.5×

IllustrativeFleet baseline 0.8% · 176,700 runs / 30 days2 of 5 slices over the 2.0× review threshold

Detection signal

Embedding-staleness SLA; reopen rate of already-fixed closures

Eval / control

40 retrieval cases

First response

Re-index; reopen dismissed regressions

Verification

Dismissed regressions re-checked against the freshly indexed corpus; index age re-measured against its staleness SLA

QAD-30Root-cause misattribution — AI incident summaries blame the wrong subsystemSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Multi-service distributed incidents	49,400	6.0%	3.3×
Incidents during simultaneous deploys	23,600	4.8%	2.7×
Infrastructure and dependency-level failures	12,500	3.6%	2.0×
Incidents without distributed tracing	17,300	2.2%	1.2×
Single-service traced incidents	78,200	1.0%	0.6×

IllustrativeFleet baseline 1.8% · 181,000 runs / 30 days3 of 5 slices over the 2.0× review threshold

Detection signal

RCA audit vs. human-validated causes; recurrence tracking

Eval / control

40 RCA replays

First response

Correct records; causal claims labeled as hypotheses

Verification

Corrected cause re-tested by reproducing the failure in the named subsystem; recurrence tracked across releases

QAD-31Repro-context evaporation — summaries drop environment and repro stepsSEV-3▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Long multi-comment defect threads	46,700	3.8%	3.2×
Reports with attached environment dumps	22,400	3.1%	2.6×
Cross-language and localized reports	11,800	2.3%	1.9×
Digest and rollup summarization runs	16,400	1.7%	1.4×
Structured-template single-report summaries	88,100	0.6%	0.5×

IllustrativeFleet baseline 1.2% · 185,400 runs / 30 days2 of 5 slices over the 2.0× review threshold

Detection signal

Field-level diff on env/version/steps; cannot-reproduce close rate

Eval / control

40 summarization cases

First response

Restore from originals; enforce structured-field summarization

Verification

Restored fields re-checked against the original report; cannot-reproduce closure rate re-measured over a fresh window

QAD-32Slop flooding — hallucinated AI reports drown real triage capacitySEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Public bug-bounty intake queues	53,600	2.2%	3.7×
Open-source public issue trackers	21,600	1.5%	2.5×
Automated scanner-generated findings	13,600	1.1%	1.8×
Unscreened first-time reporter submissions	15,800	0.8%	1.3×
Repro-gated internal intake	85,100	0.3%	0.5×

IllustrativeFleet baseline 0.6% · 189,700 runs / 30 days2 of 5 slices over the 2.0× review threshold

Detection signal

Invalid-report ratio; triage-hours asymmetry per closed-invalid

Eval / control

40 screening cases

First response

Pre-triage screening gate; repro required before human time

Verification

Screened queue re-sampled for genuine defects wrongly rejected; invalid-report ratio re-measured after the gate lands

QAD-33Confidentiality leakage — restricted data surfaces in triage digestsSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Digests spanning several projects	54,000	5.7%	3.6×
Embargoed security defect threads	21,700	4.5%	2.8×
Reports containing customer production data	13,700	2.9%	1.8×
Broadly distributed status digests	18,900	2.1%	1.3×
Single-project scoped digests	85,700	0.9%	0.6×

IllustrativeFleet baseline 1.6% · 194,000 runs / 30 days2 of 5 slices over the 2.0× review threshold

Detection signal

Canary documents; scope audit of summarizer identity

Eval / control

30 canary-document cases

First response

Recall digests; least-privilege re-scope; breach review

Verification

Canary documents re-planted after re-scoping; recalled digests confirmed removed from every downstream store

Physical inspection & vision QA

QAD-34Scene drift — lighting, camera or material-lot shift silently degrades the modelSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Night and third-shift production	54,100	3.4%	3.4×
Supplier and material lot changes	25,900	2.7%	2.7×
Post-maintenance fixture reinstallation	13,700	2.0%	2.0×
Seasonal ambient condition swings	19,000	1.3%	1.3×
Validated stable reference lines	85,600	0.5%	0.5×

IllustrativeFleet baseline 1.0% · 198,300 runs / 30 days3 of 5 slices over the 2.0× review threshold

Detection signal

SPC on confidence-score distributions vs. validation-time limits

Eval / control

60 drift cases (lot / lighting / fixture)

First response

Hold verdicts; recalibrate and revalidate before resume

Verification

Recalibrated station revalidated against reference lots; control charts re-established inside limits before verdicts resume

QAD-35Novel-defect blindness — defect classes outside training data pass as normalSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
New product introduction ramps	13,500	6.5%	3.2×
Newly qualified supplier components	6,500	5.2%	2.6×
Process or tooling change windows	4,100	4.0%	2.0×
Rare intermittent field failures	4,800	2.9%	1.4×
Mature high-volume steady lines	25,600	1.0%	0.5×

IllustrativeFleet baseline 2.0% · 54,500 runs / 30 days3 of 5 slices over the 2.0× review threshold

Detection signal

Out-of-distribution scoring on passed units; human audit of pass sample

Eval / control

40 novel-defect probes

First response

Contain affected lots; retrain with the new class

Verification

Retrained model re-scored on the new defect class; contained lots re-inspected before containment lifts

QAD-36Silent input degradation — fogged optics still yield confident passesSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Particulate-heavy production areas	16,600	4.4%	3.1×
Long uninterrupted production shifts	6,700	3.5%	2.5×
Coolant and lubricant spray zones	4,200	2.7%	1.9×
Stations without independent quality telemetry	4,900	2.0%	1.4×
Regularly serviced monitored stations	26,400	0.8%	0.6×

IllustrativeFleet baseline 1.4% · 58,800 runs / 30 days2 of 5 slices over the 2.0× review threshold

Detection signal

Per-frame image-quality telemetry independent of verdicts

Eval / control

30 image-quality cases

First response

Stop line verdicts; maintenance; re-inspect the affected window

Verification

Optics re-checked against the image-quality floor after maintenance; the affected window re-inspected unit by unit

QAD-37False-reject storm — false calls flood verifiers into bulk-passingSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Newly retuned detection thresholds	17,200	2.9%	3.6×
Cosmetic and appearance defect classes	8,200	1.9%	2.4×
Peak-throughput production periods	4,400	1.5%	1.9×
Single-verifier staffing windows	6,000	1.1%	1.4×
Stable calibrated critical-defect checks	27,300	0.5%	0.6×

IllustrativeFleet baseline 0.8% · 63,100 runs / 30 days2 of 5 slices over the 2.0× review threshold

Detection signal

False-call-to-defect ratio; verifier queue length

Eval / control

40 false-call cases

First response

Re-threshold; audit bulk-passed units

Verification

Bulk-passed units pulled and re-inspected manually; false-call ratio re-measured after the threshold is re-tuned

QAD-38Minority-class suppression — rare critical defects under-detectedSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Rare safety-critical defect classes	17,000	6.2%	3.4×
Aggregate-accuracy optimized models	8,100	5.0%	2.8×
Newly added defect categories	4,300	3.1%	1.7×
Visually subtle defect types	6,000	2.3%	1.3×
Frequent high-contrast defect classes	32,000	1.0%	0.6×

IllustrativeFleet baseline 1.8% · 67,400 runs / 30 days2 of 5 slices over the 2.0× review threshold

Detection signal

Per-class recall gates, not aggregate accuracy

Eval / control

40 per-class recall cases

First response

Rebalance training; recall floor per critical class

Verification

Per-class recall re-measured on the rebalanced model; rare critical classes must clear their floor individually

QAD-39Label-noise propagation — annotation errors baked into the modelSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Ambiguous borderline severity labels	20,300	4.0%	3.3×
Large outsourced annotation batches	8,200	3.2%	2.7×
Newly onboarded annotator cohorts	5,100	2.4%	2.0×
Classes with similar visual signatures	6,000	1.5%	1.2×
Expert-adjudicated consensus labels	32,200	0.6%	0.5×

IllustrativeFleet baseline 1.2% · 71,800 runs / 30 days3 of 5 slices over the 2.0× review threshold

Detection signal

Inter-annotator kappa audits; error clustering by class

Eval / control

30 label-audit cases

First response

Relabel affected classes; retrain

Verification

Relabeled set re-scored for annotator agreement; the retrained model re-validated on a held-out audit sample

QAD-40Golden-sample overfitting — tuned to pristine references, rejects normal variationSEV-3▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Curated pristine reference sets	21,200	1.9%	3.2×
Early-deployment models on new lines	8,500	1.5%	2.5×
Cosmetic tolerance-band judgements	5,400	1.2%	2.0×
Multi-plant model transfers	7,400	0.9%	1.5×
Production-sampled training baselines	33,600	0.3%	0.5×

IllustrativeFleet baseline 0.6% · 76,100 runs / 30 days3 of 5 slices over the 2.0× review threshold

Detection signal

Golden-set vs. line-accuracy gap

Eval / control

30 known-good challenge lots

First response

Re-tune on production variation

Verification

Re-tuned model re-run on known-good challenge lots; the golden-versus-line accuracy gap re-measured back to tolerance

Fleet & oversight operations

QAD-41Cross-agent verdict divergence — QA agents disagree; orchestrator picks silentlySEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Ambiguous acceptance-criteria releases	21,900	5.9%	3.7×
Agents on different model families	10,500	3.9%	2.4×
Borderline severity boundary cases	5,500	3.0%	1.9×
Silent orchestrator tie-breaking paths	7,700	2.2%	1.4×
Single-arbiter documented decisions	34,700	0.9%	0.6×

IllustrativeFleet baseline 1.6% · 80,300 runs / 30 days2 of 5 slices over the 2.0× review threshold

Detection signal

Inter-agent agreement (kappa) per release; conflict logging

Eval / control

40 consensus cases

First response

Conflicts escalate to humans; arbiter rules documented

Verification

Escalated conflicts re-decided by named humans; inter-agent agreement re-measured on the next release consensus set

QAD-42Model-update regression — provider drift shifts verdicts without revalidationSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Runs on floating provider aliases	21,000	3.5%	3.5×
Post-rollout adoption windows	10,100	2.8%	2.8×
Judgement-heavy severity scoring tasks	6,300	1.8%	1.8×
Rarely exercised evaluation paths	7,400	1.3%	1.3×
Pinned canary-validated versions	39,800	0.6%	0.6×

IllustrativeFleet baseline 1.0% · 84,600 runs / 30 days2 of 5 slices over the 2.0× review threshold

Detection signal

Golden-set canary re-runs on model-fingerprint change

Eval / control

60 golden-set canaries

First response

Pin versions; revalidate before adoption

Verification

Golden set re-scored on the candidate fingerprint; verdicts issued before revalidation re-checked and reissued

QAD-43Verdict rubber-stamping — human sign-off stops verifying agent outputSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
High-volume routine pass verdicts	25,100	6.8%	3.4×
Long-tenured reviewer assignments	10,100	5.4%	2.7×
Sign-off under release-deadline pressure	6,400	4.1%	2.0×
Agents with strong past accuracy	7,400	2.5%	1.2×
Rotated canary-seeded reviews	39,900	1.1%	0.6×

IllustrativeFleet baseline 2.0% · 88,900 runs / 30 days3 of 5 slices over the 2.0× review threshold

Detection signal

Override rate trending to zero; review-time collapse; seeded canaries missed

Eval / control

30 seeded-canary checks

First response

Reviewer rotation and sampling duties; standing canary program

Verification

Reviewer rotation re-audited after the change; planted canaries re-checked as caught before sign-off counts again

QAD-44Runaway loop cost — retry spirals burn CI budget without progressSEV-3▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Fix-until-green autonomous loops	25,400	4.6%	3.3×
Tasks with unreachable acceptance criteria	12,200	3.6%	2.6×
Large repository build-and-test cycles	6,400	2.8%	2.0×
Nightly unattended pipeline runs	8,900	2.0%	1.4×
Capped bounded-iteration tasks	40,300	0.7%	0.5×

IllustrativeFleet baseline 1.4% · 93,200 runs / 30 days3 of 5 slices over the 2.0× review threshold

Detection signal

Token/cost ceilings; no-progress iteration counter

Eval / control

20 budget scenarios

First response

Circuit breakers; per-task spend caps

Verification

Breaker exercised on the replayed spiral; CI spend per task re-measured under the new cap

A · Test-case generation agents

QS-03Contract inferred, not read01 · RetrievalSEV-3▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Untraced legacy modules	1,700	7.2%	3.6×
Interfaces with no written specification	1,400	5%	2.5×
Modules with a single caller	1,200	3.6%	1.8×
Interfaces documented in a comment	1,000	2.6%	1.3×
Interfaces with a written contract	10,000	0.6%	0.3×

IllustrativeFleet baseline 2% · 15,300 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

FDA, General Principles of Software Validation (11 Jan 2002) § 5.2.5 — separates specification-based black-box testing from code-based structural testing, and they are not substitutes

Detection signal

Source of the contract behind every generated test; tests whose expectations derive only from call sites

Eval / control

Contract-provenance eval: tests state whether the contract was read or inferred

First response

Mark inferred contracts on the test; escalate modules with no written one

Verification

Contract provenance re-measured per module; every suite built on an inferred contract re-derived once a written specification exists

Test-case generation agents · full lifecycle plate on the agent page →

QS-04Assertion froze the defect02 · ReasoningSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Code with an open defect	1,800	9.8%	3.8×
Tests written from current behaviour	1,500	6.8%	2.6×
Concurrency and timing paths	1,300	4.9%	1.9×
Tests generated in a coverage sprint	1,100	3.6%	1.4×
Tests written from a specification	10,100	0.9%	0.3×

IllustrativeFleet baseline 2.6% · 15,800 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

Liu et al., ISSTA 2023 — under realistic evaluation the neural oracle generator scored 0.38% precision, roughly two hundred and sixty false alarms for every real bug found

Detection signal

Expected values sourced from a run rather than a specification; assertions on modules with an open defect

Eval / control

Oracle-provenance eval: an expectation comes from a specification, never a run

First response

Block generation against modules with an open defect; source expectations

Verification

Oracle provenance re-measured per suite; every assertion frozen from a buggy run re-derived from the specification and the defect re-exposed

Test-case generation agents · full lifecycle plate on the agent page →

QS-06Fixture masks the case02 · ReasoningSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Fixtures matching any input	1,700	8.5%	3.7×
Wildcards in a matcher	1,400	5.8%	2.5×
Fixtures shared across a suite	1,200	4.2%	1.8×
Fixtures generated with the tests	1,000	3%	1.3×
Fixtures pinned to explicit values	10,200	0.8%	0.3×

IllustrativeFleet baseline 2.3% · 15,500 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

The CrowdStrike channel file 291 root cause analysis (6 Aug 2024) — a wildcard in the twenty-first test field meant every run passed over an out-of-bounds read

Detection signal

Wildcard and permissive matchers per fixture; fixtures that pass against a deliberately wrong input

Eval / control

Mutation eval on fixtures: a fixture that cannot fail is reported as a gap

First response

Reject wildcards in generated fixtures; mutate-test every new fixture

Verification

Fixture mutation results re-measured per suite; every fixture that survived a wrong input rewritten to explicit values before it gates a release

Test-case generation agents · full lifecycle plate on the agent page →

QS-02Merged without a person03 · Tool / writeSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Merges completed by the agent	1,700	8%	3.6×
Merges on a green pipeline only	1,400	5.5%	2.5×
Merges outside working hours	1,200	4%	1.8×
Repositories with auto-merge enabled	1,000	2.9%	1.3×
Merges with a named approver	10,300	0.7%	0.3×

IllustrativeFleet baseline 2.2% · 15,600 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

Alshahwan et al., FSE 2024 (Meta test generation) — seventy-three percent of recommendations reached production, but only behind mechanical improvement filters plus engineer acceptance

Detection signal

Named approver recorded per merge; merges whose only gate was a passing pipeline run

Eval / control

Approval eval: a named engineer approves, and a pipeline never substitutes

First response

Require a named approver on every merge; disable agent auto-merge

Verification

Named-approver presence re-measured per merge; every agent-completed merge re-reviewed before the suite is trusted as a gate

Test-case generation agents · full lifecycle plate on the agent page →

QS-05Test weakened silently03 · Tool / writeSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Assertions rewritten after a failure	1,700	6.5%	3.6×
Tests relaxed to unblock a release	1,400	4.5%	2.5×
Assertions loosened to a range	1,200	3.2%	1.8×
Tests marked as expected failures	1,000	2.3%	1.3×
Assertions unchanged since written	10,400	0.5%	0.3×

IllustrativeFleet baseline 1.8% · 15,700 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

21 CFR 11.10(e) — audit trails must be computer-generated and record changes must not obscure previously recorded information, and a code repository is not that system

Detection signal

Assertion strength diffed across revisions; tests whose expectations widened after a failing run

Eval / control

Strength eval: a weakened assertion is flagged, never silently accepted

First response

Diff assertion strength on every change; require a reason for any relaxation

Verification

Assertion-strength diffs re-run across the suite history; every silently weakened test restored or its relaxation justified on the record

Test-case generation agents · full lifecycle plate on the agent page →

QS-01Approved unread04 · OutputSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Approvals inside ten seconds	1,800	7.6%	3.6×
Suites of more than fifty tests	1,500	5.2%	2.5×
Approvals batched at end of sprint	1,300	3.8%	1.8×
Approvals by an engineer on another team	1,000	2.7%	1.3×
Approvals with recorded review notes	10,500	0.6%	0.3×

IllustrativeFleet baseline 2.1% · 16,100 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

21 CFR 11.50(a) — a signed electronic record must carry the printed name of the signer, a timestamp, and the meaning of the signature: review, approval, responsibility or authorship

Detection signal

Approval dwell against suite size; approvals with no review comment or change requested

Eval / control

Review-depth eval: dwell correlated with the defects review should catch

First response

Floor the dwell per test count; cap generated suite size per review

Verification

Approval dwell re-measured against suite size; every suite approved inside the floor re-read before it is allowed to gate a release

Test-case generation agents · full lifecycle plate on the agent page →

QS-07Silent coverage drift05 · Change / VersionSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Suites after a model or rule change	1,700	6.1%	3.6×
Modules whose coverage rose with no new tests	1,400	4.2%	2.5×
Suites gated on a coverage threshold	1,200	3%	1.8×
Modules with an open defect	1,000	2.2%	1.3×
Suites pinned to a model version	10,400	0.5%	0.3×

IllustrativeFleet baseline 1.7% · 15,700 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

Zhao, Zhou and Cohen, ISSTA 2026 — across three hundred and eighteen buggy focal methods, coverage and mutation stop being reliable indicators precisely when the code is buggy

Detection signal

Assertion breadth per release against the previous one; coverage rising with no corresponding test additions

Eval / control

Version-conformance eval on a fixed module set at every model change

First response

Pin the model version; re-run the fixed set before any model update lands

Verification

Fixed-set assertion breadth re-measured across two model versions; every module whose coverage moved without new tests re-examined by mutation

Test-case generation agents · full lifecycle plate on the agent page →

B · Defect-triage assistants

VE-03The report that never reached the tracker01 · RetrievalSEV-3▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Injury-adjacent complaints	1,700	7%	3.7×
Reports arriving by email or chat	1,400	4.9%	2.6×
Reports received out of hours	1,200	3.4%	1.8×
Reports routed to a shared mailbox	1,000	2.5%	1.3×
Reports raised through the tracker	10,300	0.6%	0.3×

IllustrativeFleet baseline 1.9% · 15,600 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

21 CFR 803.17 requires written procedures for timely identification, communication and evaluation of reportable events — a thread nobody triages meets none of them

Detection signal

Intake channel coverage against reports received; complaints sitting outside the tracker with a clock running

Eval / control

Coverage eval reconciling every inbound channel to the tracker

First response

Sweep every channel into the tracker; name an owner per channel

Verification

Channel coverage re-measured per period; every report found outside the tracker logged and assessed inside the window its clock started

Defect-triage assistants · full lifecycle plate on the agent page →

VE-04Merged away with the injury line02 · ReasoningSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Duplicate-cluster outliers	1,800	7.8%	3.7×
Clusters merged on similar wording	1,400	5.5%	2.6×
Clusters covering several patients	1,500	4.4%	2.1×
Clusters merged during a backlog	1,100	2.9%	1.4×
Clusters merged on the same event	10,200	0.6%	0.3×

IllustrativeFleet baseline 2.1% · 16,000 runs / 30 days3 of 5 slices over the 2.0× review threshold

In the wild

21 CFR 803.22 permits one report only where multiple sources concern the same patient and the same reportable event — dedup beyond that drops a report

Detection signal

Patient and event identity per merged cluster; merges combining reports about different patients or events

Eval / control

Merge eval on patient and event identity, not on text similarity

First response

Merge only on the same patient and the same event; keep members

Verification

Cluster identity re-measured per merge; every harm report absorbed into another patient's cluster separated and reported on its own clock

Defect-triage assistants · full lifecycle plate on the agent page →

VE-06Two regimes, one due date02 · ReasoningSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Cross-regime field reports	1,700	7.4%	3.7×
Products sold in several markets	1,400	5.2%	2.6×
Due dates set by a single template	1,200	3.6%	1.8×
Reports touching two agencies at once	1,000	2.6%	1.3×
Reports under a single regime	10,500	0.6%	0.3×

IllustrativeFleet baseline 2% · 15,800 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

One regulator allows thirty calendar days for a manufacturer report while another requires notice within twenty-four hours — a single countdown cannot serve both

Detection signal

Applicable regimes resolved per report against the due dates set; reports carrying one date where two duties apply

Eval / control

Deadline eval per regime, one clock per obligation and per market

First response

Record every applicable regime and its own clock; never merge dates

Verification

Applicable regimes re-measured per report; every report running on one clock recalculated before the shortest applicable deadline expires

Defect-triage assistants · full lifecycle plate on the agent page →

VE-02Severity gates the reportability read03 · Tool / writeSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Routine cosmetic defects	1,800	8.1%	3.7×
Reports banded automatically at intake	1,400	5.7%	2.6×
Reports with no injury described	1,500	4%	1.8×
Reports closed in a bulk sweep	1,100	2.9%	1.3×
Reports given a reportability read	10,200	0.7%	0.3×

IllustrativeFleet baseline 2.2% · 16,000 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

The Consumer Product Safety Commission's duty-to-report guidance states that the law does not require injury or harm to have occurred — a severity band is not a reportability answer

Detection signal

Reportability review recorded per report against severity band; low-banded reports closed with no reportability read

Eval / control

Reportability eval independent of the severity band assigned at intake

First response

Run reportability on every report; never gate it on a severity band

Verification

Reportability reads re-measured across closed reports; every low-banded report closed without one re-assessed inside its reporting window

Defect-triage assistants · full lifecycle plate on the agent page →

VE-05Cluster seen, nobody told03 · Tool / writeSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Injury-adjacent complaints	1,600	5.6%	3.7×
Trends visible only on a dashboard	1,300	3.9%	2.6×
Trends crossing product families	1,400	2.7%	1.8×
Trends emerging over several weeks	1,000	2%	1.3×
Trends routed to a named owner	10,400	0.4%	0.3×

IllustrativeFleet baseline 1.5% · 15,700 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

21 CFR 803.53 sets a five-working-day clock and recognises that awareness may come from trend analysis — a dashboard nobody reads does not start it

Detection signal

Named recipient per surfaced trend; clusters visible in the tool with no notification to a person

Eval / control

Notification eval: a trend addresses a named owner or it has not surfaced

First response

Route trends to a named owner; alert rather than display

Verification

Trend notifications re-measured per detected cluster; every trend left on a dashboard escalated inside the working-day window it started

Defect-triage assistants · full lifecycle plate on the agent page →

VE-01Closed, the deliberation unrecorded04 · OutputSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Reports closed as not reportable	1,700	7.4%	3.7×
Calls made in a review meeting	1,400	5.2%	2.6×
Calls made by a departed reviewer	1,200	3.6%	1.8×
Calls made at a period close	1,000	2.6%	1.3×
Calls recorded with their reasoning	10,500	0.6%	0.3×

IllustrativeFleet baseline 2% · 15,800 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

21 CFR 803.18 requires event files to hold all documentation of the deliberations and decision-making on reportability — a verdict on its own does not meet it

Detection signal

Deliberation recorded per closed report; not-reportable calls with no reasoning retrievable behind them

Eval / control

Closure-record eval: the report, its deliberation and the decider stored together

First response

Record the deliberation at closure; block a bare not-reportable call

Verification

Deliberation records re-measured per closed report; every call with nothing behind it reconstructed while the complaint records are retained

Defect-triage assistants · full lifecycle plate on the agent page →

VE-07Quiet accuracy drift05 · Change / VersionSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Cross-regime field reports	1,700	5.9%	3.7×
Complaint vocabulary changed by a channel	1,400	4.2%	2.6×
Products renamed between releases	1,200	2.9%	1.8×
Models trained on an earlier period	1,000	2.1%	1.3×
Models re-measured each period	10,400	0.5%	0.3×

IllustrativeFleet baseline 1.6% · 15,700 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

The FDA's 2024 guidance on predetermined change control plans asks sponsors to name the triggers for retraining when data drift is observed — silence is the default otherwise

Detection signal

Linking accuracy per period against a labelled sample; accuracy falling as complaint wording shifts

Eval / control

Drift eval on a labelled sample refreshed each period, not at launch

First response

Re-measure on a fresh labelled sample; set a retraining trigger

Verification

Linking accuracy re-measured per period; every drop below the trigger retrained before the next period's reports are linked on it

Defect-triage assistants · full lifecycle plate on the agent page →

C · Release-gate copilots

VF-02Skipped suites read as green01 · RetrievalSEV-3▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Regulated first releases	1,700	7%	3.7×
Suites skipped by an infrastructure failure	1,400	4.9%	2.6×
Suites excluded by a filter	1,200	3.4%	1.8×
Runs cut short by a timeout	1,000	2.5%	1.3×
Runs reporting planned against executed	10,300	0.6%	0.3×

IllustrativeFleet baseline 1.9% · 15,600 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

No incident on the public record turns on a pass rate computed over executed rather than planned tests; the exposure is a green gate that never counted the skips

Detection signal

Planned against executed test counts per run; gates cleared where the executed set is smaller than the plan

Eval / control

Completeness eval on the planned set, so a skip fails the gate

First response

Report planned and executed separately; fail the gate on any skip

Verification

Planned-versus-executed counts re-measured per gate; every gate cleared over skipped suites re-run before the release ships

Release-gate copilots · full lifecycle plate on the agent page →

VF-04Coverage read as correctness02 · ReasoningSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Cross-market submissions	1,800	7.8%	3.7×
Gates expressed as a single threshold	1,400	5.5%	2.6×
Coverage produced by generated tests	1,500	3.8%	1.8×
Coverage quoted in a submission	1,100	2.7%	1.3×
Gates carrying a reasoned argument	10,200	0.6%	0.3×

IllustrativeFleet baseline 2.1% · 16,000 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

The FDA's General Principles of Software Validation (11 Jan 2002) states that software cannot be exhaustively tested — coverage met is not correctness shown

Detection signal

Reasoning recorded alongside coverage per gate; releases cleared on a coverage threshold with no argument behind it

Eval / control

Gate eval requiring a reasoned argument, not a single coverage number

First response

Require a written argument per gate; treat coverage as one input

Verification

Gate arguments re-measured per release; every release cleared on coverage alone re-argued before the submission relies on it

Release-gate copilots · full lifecycle plate on the agent page →

VF-05Anomaly listed, never evaluated02 · ReasoningSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Regulated first releases	1,700	7.4%	3.7×
Anomalies carried from a prior release	1,400	5.2%	2.6×
Anomalies listed in bulk	1,200	3.6%	1.8×
Anomalies with no reproduction steps	1,000	2.6%	1.3×
Anomalies with an impact evaluation	10,500	0.6%	0.3×

IllustrativeFleet baseline 2% · 15,800 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

The FDA's device software premarket guidance of 14 June 2023 requires each unresolved anomaly to carry an impact evaluation — a bare list is not that

Detection signal

Impact evaluation recorded per listed anomaly; unresolved anomalies shipped with no assessment beside them

Eval / control

Anomaly eval requiring an impact statement per item, not per list

First response

Evaluate each anomaly individually; block a release on a bare list

Verification

Anomaly evaluations re-measured per release; every unevaluated anomaly assessed before the submission or the release is relied on

Release-gate copilots · full lifecycle plate on the agent page →

VF-03Override left off the record03 · Tool / writeSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Waived exit criteria	1,800	8.1%	3.7×
Waivers agreed verbally in a call	1,400	5.7%	2.6×
Waivers taken outside the gate tool	1,500	4%	1.8×
Waivers taken out of hours	1,100	2.9%	1.3×
Waivers recorded in the gate record	10,200	0.7%	0.3×

IllustrativeFleet baseline 2.2% · 16,000 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

21 CFR 11.10(e) requires time-stamped audit trails of operator actions and bars changes that obscure prior entries — an unlogged waiver defeats both

Detection signal

Waiver records against gates cleared with unmet criteria; releases shipped over a criterion with no waiver logged

Eval / control

Override eval: an unmet criterion clears only through a recorded waiver

First response

Log every override in the gate record; block an unrecorded clear

Verification

Waiver records re-measured per release; every unlogged override reconstructed while the gate and build records are retained

Release-gate copilots · full lifecycle plate on the agent page →

VF-07Approval logged without attribution03 · Tool / writeSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Cross-market submissions	1,600	5.6%	3.7×
Approvals applied by a shared account	1,300	3.9%	2.6×
Approvals applied by an automation	1,400	2.7%	1.8×
Approvals recorded without a role	1,000	2%	1.3×
Approvals recording name, role and time	10,400	0.4%	0.3×

IllustrativeFleet baseline 1.5% · 15,700 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

21 CFR 11.50 requires a signed record to show the signer's printed name, the date and time, and the meaning of the signature — approval alone shows none of them

Detection signal

Signer identity, role and time per logged approval; approvals recorded with no attributable person behind them

Eval / control

Signature eval requiring name, role, time and meaning together

First response

Record name, role and meaning at approval; block a shared account

Verification

Approval attributions re-measured per release; every unattributed approval reconstructed or re-taken before the release record is submitted

Release-gate copilots · full lifecycle plate on the agent page →

VF-06Criteria aimed at the wrong object04 · OutputSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Routine patch releases	1,700	7.4%	3.7×
Criteria inherited from a prior release	1,400	5.2%	2.6×
Criteria written against a component	1,200	3.6%	1.8×
Builds assembled from several artefacts	1,000	2.6%	1.3×
Criteria written against the shipped build	10,500	0.6%	0.3×

IllustrativeFleet baseline 2% · 15,800 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

FAA Advisory Circular 20-115D (21 July 2017) accepts the recognised software standard for the software aspects of certification — credit attaches to the build, not to a proxy

Detection signal

Gate criteria resolved against the artefact that ships; criteria evaluated on a component the release does not contain

Eval / control

Object eval binding each criterion to the shipped artefact identifier

First response

Bind criteria to the shipped build; refuse a proxy artefact

Verification

Criterion-to-artefact binding re-measured per release; every criterion aimed at another object re-run against the build that actually ships

Release-gate copilots · full lifecycle plate on the agent page →

VF-01Superseded rule read as live05 · Change / VersionSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Regulated first releases	1,700	5.9%	3.7×
Gate templates carried between products	1,400	4.2%	2.6×
Rules copied into a quality plan	1,200	2.9%	1.8×
Rules changed by a restructuring	1,000	2.1%	1.3×
Rules re-read from the regulation	10,400	0.5%	0.3×

IllustrativeFleet baseline 1.6% · 15,700 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

The FDA's quality management system rule, published 2 Feb 2024, reserved the old design-controls section — citing design controls there now cites an empty section

Detection signal

Cited section status per gate criterion; criteria citing a section the restructuring reserved or removed

Eval / control

Currency eval reading the regulation, not the stored quality plan

First response

Re-read cited sections each cycle; retire criteria citing empty ones

Verification

Cited sections re-checked per gate template; every criterion resting on a reserved section re-derived before the next release runs against it

Release-gate copilots · full lifecycle plate on the agent page →

D · Inspection-support agents

QI-01Superseded drawing revision01 · RetrievalSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
First-off after changeover	1,700	7.4%	3.7×
Drawings revised by engineering	1,400	5.2%	2.6×
Revisions cached at station setup	1,200	3.6%	1.8×
Parts made to a customer print	1,000	2.6%	1.3×
Revisions re-read at each lot	10,500	0.6%	0.3×

IllustrativeFleet baseline 2% · 15,800 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

No incident on the public record turns on an agent checking a part against a superseded drawing revision; the exposure is conformance judged to the wrong baseline

Detection signal

Drawing revision at inspection against the revision released; lots inspected on a superseded baseline

Eval / control

Freshness eval: the revision resolves at lot start or inspection holds

First response

Re-read the revision at each lot; refuse a cached drawing

Verification

Drawing revisions re-checked per lot; every lot inspected on a superseded baseline re-inspected before it is released to the customer

Inspection-support agents · full lifecycle plate on the agent page →

QI-02Angle the station cannot see01 · RetrievalSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
New SKU, first lot	1,800	7.8%	3.7×
Parts with an internal feature	1,400	5.5%	2.6×
Parts presented in one orientation	1,500	3.8%	1.8×
Stations built for a prior part	1,100	2.7%	1.3×
Stations with full-coverage views	10,200	0.6%	0.3×

IllustrativeFleet baseline 2.1% · 16,000 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

No incident on the public record turns on a defect visible only from a view the inspection cell never captures; the exposure is a blind angle read as a pass

Detection signal

View coverage per part geometry; defect classes whose visible surfaces no station view reaches

Eval / control

Coverage eval mapping each defect class to a captured view

First response

Map views to defect classes at setup; flag uncovered surfaces

Verification

View coverage re-measured per part introduction; every uncovered defect class given a view or an offline check before the lot is released

Inspection-support agents · full lifecycle plate on the agent page →

QI-03Rare defect class missed02 · ReasoningSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Rare defect classes	1,800	8.1%	3.7×
Classes seen once in a year	1,400	5.7%	2.6×
Classes present only at one supplier	1,500	4%	1.8×
Classes merged into a general label	1,100	2.9%	1.3×
Classes with many labelled examples	10,200	0.7%	0.3×

IllustrativeFleet baseline 2.2% · 16,000 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

No incident on the public record turns on a defect class with too few labelled examples to be presented; the exposure is a rare failure the station never proposes

Detection signal

Per-class recall against label counts; classes whose recall cannot be measured for want of examples

Eval / control

Per-class eval, so a class with too few examples fails rather than passes

First response

Report per-class recall; route thin classes to a human check

Verification

Per-class recall re-measured each cycle; every class too thin to measure routed to manual inspection until enough examples exist

Inspection-support agents · full lifecycle plate on the agent page →

QI-04Record ties to no part03 · Record / fileSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
First-off after changeover	1,600	5.6%	3.7×
Parts without a unit marking	1,300	3.9%	2.6×
Records keyed to a time window	1,400	2.7%	1.8×
Lots split across two stations	1,000	2%	1.3×
Records keyed to a unit identifier	10,400	0.4%	0.3×

IllustrativeFleet baseline 1.5% · 15,700 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

21 CFR part 830 splits the unique device identifier into a device identifier and a production identifier carrying the lot or serial — a record without the latter names no unit

Detection signal

Unit and station resolved per inspection record; records that cannot be tied to a specific part

Eval / control

Binding eval on unit identity, not on the time the record was written

First response

Key records to a unit and station identifier; block a time-only key

Verification

Record-to-unit resolution re-measured per lot; every untied record reconstructed before the lot's traceability file is closed

Inspection-support agents · full lifecycle plate on the agent page →

QI-05Reading without gauge capability04 · OutputSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
New SKU, first lot	1,700	7.4%	3.7×
Measurements from a new fixture	1,400	5.2%	2.6×
Measurements near the tolerance edge	1,200	3.6%	1.8×
Measurements taken by a vision system	1,000	2.6%	1.3×
Measurements from a studied gauge	10,500	0.6%	0.3×

IllustrativeFleet baseline 2% · 15,800 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

NIST Technical Note 1297 states that a measurement result is complete only when accompanied by a statement of its uncertainty — a bare reading is not complete

Detection signal

Capability study present per reporting gauge; measurements reported with no uncertainty statement behind them

Eval / control

Capability eval before a gauge reports, not after a dispute

First response

Study the gauge before it reports; publish the uncertainty with it

Verification

Gauge capability re-measured per station; every measurement reported from an unstudied gauge re-taken before the lot is dispositioned on it

Inspection-support agents · full lifecycle plate on the agent page →

QI-06False rejects train waving through04 · OutputSEV-3▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Mature SKU, settled line	1,700	7%	3.7×
Stations tuned for high sensitivity	1,400	4.9%	2.6×
Shifts with a high flag volume	1,200	3.4%	1.8×
Flags cleared in a bulk action	1,000	2.5%	1.3×
Stations with a measured false-reject rate	10,300	0.6%	0.3×

IllustrativeFleet baseline 1.9% · 15,600 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

No incident on the public record turns on inspectors desensitised by an agent's false rejects; the exposure is a flag rate high enough that flags stop being read

Detection signal

False-reject rate against inspector clear-through time; flags cleared faster than they can be examined

Eval / control

Human-factors eval on flag volume and clearing time, not only accuracy

First response

Cap the flag rate per shift; measure how fast flags are cleared

Verification

False-reject rates and clearing times re-measured per shift; every station over the cap retuned before its flags stop being examined

Inspection-support agents · full lifecycle plate on the agent page →

QI-07Drift after a rebuild05 · Change / VersionSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
First-off after changeover	1,800	7.8%	3.7×
Stations rebuilt after maintenance	1,400	5.5%	2.6×
Material changed by a new supplier	1,500	3.8%	1.8×
Changes made without a revalidation	1,100	2.7%	1.3×
Stations revalidated after each change	10,200	0.6%	0.3×

IllustrativeFleet baseline 2.1% · 16,000 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

The NIST AI Risk Management Framework (Jan 2023) notes that drift in data, model or concept drives more frequent maintenance — a rebuilt station moves without saying so

Detection signal

Station performance before and after each physical change; call rates shifting with no revalidation recorded

Eval / control

Revalidation eval triggered by any optical, fixture or material change

First response

Revalidate after every physical change; hold the station until it passes

Verification

Station performance re-measured after each rebuild; every station whose calls moved revalidated before the next lot is released on it

Inspection-support agents · full lifecycle plate on the agent page →

E · Requirements-trace agents

VH-03Live view read as the record01 · RetrievalSEV-3▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Changed safety requirements	1,700	7%	3.7×
Views regenerated on each open	1,400	4.9%	2.6×
Records exported after the review	1,200	3.4%	1.8×
Tools edited by several teams	1,000	2.5%	1.3×
Records read from the controlled file	10,300	0.6%	0.3×

IllustrativeFleet baseline 1.9% · 15,600 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

No incident on the public record turns on a tool's live view being read instead of the controlled file; the exposure is evidence that changes after it is reviewed

Detection signal

Source of each reviewed matrix; reviews performed against a live view rather than a baselined export

Eval / control

Baseline eval: a review reads a baselined file, never a live query

First response

Baseline the matrix before review; read the controlled file only

Verification

Review sources re-measured per traceability review; every review taken on a live view repeated against the baselined record

Requirements-trace agents · full lifecycle plate on the agent page →

VH-04Link asserted on wording alone02 · ReasoningSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Clinically worded requirements	1,800	7.8%	3.7×
Requirements sharing common vocabulary	1,400	5.5%	2.6×
Links proposed in a bulk pass	1,500	3.8%	1.8×
Links proposed across abstraction levels	1,100	2.7%	1.3×
Links confirmed against the artefact	10,200	0.6%	0.3×

IllustrativeFleet baseline 2.1% · 16,000 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

The NASA Systems Engineering Handbook defines verification as showing proof of compliance with requirements — wording that resembles a requirement is not proof

Detection signal

Confirmation evidence per accepted link; links accepted on text similarity with no artefact examined

Eval / control

Link eval requiring the artefact to be read, not the wording matched

First response

Confirm each link against the artefact; queue similarity proposals

Verification

Link evidence re-measured per matrix; every link accepted on wording alone re-confirmed before the matrix supports a verification claim

Requirements-trace agents · full lifecycle plate on the agent page →

VH-06Orphan closed by nearest match02 · ReasoningSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Derived low-level requirements	1,700	7.4%	3.7×
Orphans closed in a cleanup pass	1,400	5.2%	2.6×
Orphans closed against a deadline	1,200	3.6%	1.8×
Orphans with a plausible neighbour	1,000	2.6%	1.3×
Orphans reported as gaps	10,500	0.6%	0.3×

IllustrativeFleet baseline 2% · 15,800 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

No incident on the public record turns on an orphan requirement closed by a nearest-match link; the exposure is a real coverage gap that is recorded as covered

Detection signal

Orphan resolution method per closed gap; orphans closed by proximity rather than by new coverage

Eval / control

Gap eval: an orphan closes only when coverage is created, not matched

First response

Report orphans as gaps; refuse a nearest-match closure

Verification

Orphan closures re-measured per matrix; every gap closed by proximity reopened and covered before the verification argument is signed

Requirements-trace agents · full lifecycle plate on the agent page →

VH-02Reverse direction never drawn03 · Tool / writeSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Stable functional requirements	1,800	8.1%	3.7×
Matrices generated forward only	1,400	5.7%	2.6×
Artefacts added after the matrix	1,500	4%	1.8×
Code written ahead of a requirement	1,100	2.9%	1.3×
Matrices drawn in both directions	10,200	0.7%	0.3×

IllustrativeFleet baseline 2.2% · 16,000 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

NASA's software engineering handbook requires bi-directional traceability between software elements — forward links on their own do not satisfy it

Detection signal

Backward links present per artefact; artefacts with no parent requirement recorded against them

Eval / control

Bidirectional eval, so an unparented artefact fails the matrix

First response

Draw both directions; report artefacts with no parent as findings

Verification

Backward links re-measured per matrix; every unparented artefact traced or removed before the verification argument is signed

Requirements-trace agents · full lifecycle plate on the agent page →

VH-05Derived requirement given a parent03 · Tool / writeSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Derived low-level requirements	1,600	5.6%	3.7×
Derived requirements added late	1,300	3.9%	2.6×
Matrices scored on orphan count	1,400	2.7%	1.8×
Derived requirements from an implementation	1,000	2%	1.3×
Derived requirements marked as derived	10,400	0.4%	0.3×

IllustrativeFleet baseline 1.5% · 15,700 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

NASA's software engineering handbook describes derived requirements as ones not explicitly stated in the top-level system requirements — there is no parent to attach them to

Detection signal

Derived requirements carrying a parent link; derived items filed under a top-level requirement that never stated them

Eval / control

Classification eval separating derived requirements from decomposed ones

First response

Mark derived requirements as derived; escalate them for assessment

Verification

Derived classifications re-measured per matrix; every derived requirement given a false parent re-marked and escalated for a safety assessment

Requirements-trace agents · full lifecycle plate on the agent page →

VH-01Signed over links nobody read04 · OutputSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Changed safety requirements	1,700	7.4%	3.7×
Matrices running to thousands of links	1,400	5.2%	2.6×
Matrices signed at a submission deadline	1,200	3.6%	1.8×
Matrices signed by a departing owner	1,000	2.6%	1.3×
Matrices signed after a sampled review	10,500	0.6%	0.3×

IllustrativeFleet baseline 2% · 15,800 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

No incident on the public record turns on a signature over an unread traceability file; the exposure is an approval attesting to links that nobody checked

Detection signal

Review evidence per signed matrix; signatures applied with no sampling or review record behind them

Eval / control

Signature eval requiring a recorded review method and sample size

First response

Sample and record the review; block a signature with no method

Verification

Review records re-measured per signed matrix; every signature with no review behind it re-taken after a recorded sampled review

Requirements-trace agents · full lifecycle plate on the agent page →

VH-07A change leaves the links behind05 · Change / VersionSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Changed safety requirements	1,700	5.9%	3.7×
Requirements edited in place	1,400	4.2%	2.6×
Requirements changed after baseline	1,200	2.9%	1.8×
Changes made across several requirements	1,000	2.1%	1.3×
Links re-verified on each change	10,400	0.5%	0.3×

IllustrativeFleet baseline 1.6% · 15,700 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

NASA's systems-engineering procedural requirements, effective 5 July 2023, require bidirectional traceability and systematic control of changes — traceability is maintained, not built

Detection signal

Link validity after each requirement edit; links still marked valid where the requirement text has changed

Eval / control

Propagation eval: a requirement edit expires every link drawn from it

First response

Expire links on any wording change; re-verify before re-approving

Verification

Link validity re-measured per requirement change; every link left behind an edit re-verified before the matrix supports a verification claim

Requirements-trace agents · full lifecycle plate on the agent page →

F · Visual-inspection models (AOI / vision)

VI-03Truth set by the same eyes01 · RetrievalSEV-3▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Catalogued defect classes	1,700	7%	3.7×
Truth labelled by the training team	1,400	4.9%	2.6×
Truth taken from prior model output	1,200	3.4%	1.8×
Studies run on the training period	1,000	2.5%	1.3×
Studies with independent truthing	10,300	0.6%	0.3×

IllustrativeFleet baseline 1.9% · 15,600 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

The FDA's computer-assisted detection performance guidance (Sept 2022) states that clinicians in the truthing process should not be the same as those in the core performance assessment

Detection signal

Independence between truthing and assessment per study; studies whose truth came from the training team

Eval / control

Independence eval: truth is set by people outside the model's development

First response

Separate truthing from development; record who set the truth

Verification

Truthing independence re-measured per validation study; every study truthed by its own developers re-run with an independent panel before deployment

Visual-inspection models (AOI / vision) · full lifecycle plate on the agent page →

VI-04Accuracy read off a thin class02 · ReasoningSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Unseen defect classes	1,800	7.8%	3.7×
Lines with a very low defect rate	1,400	5.5%	2.6×
Studies reporting one overall figure	1,500	3.8%	1.8×
Studies run on a balanced sample	1,100	2.7%	1.3×
Studies reporting per-class results	10,200	0.6%	0.3×

IllustrativeFleet baseline 2.1% · 16,000 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

The FDA's statistical guidance on diagnostic-test studies (13 Mar 2007) states that measures of overall agreement are not in themselves a sufficient characterisation of a test

Detection signal

Per-class sensitivity against overall accuracy; models cleared on an overall figure at a low defect rate

Eval / control

Per-class eval reporting sensitivity separately from overall agreement

First response

Report per-class sensitivity; never clear on overall accuracy alone

Verification

Per-class results re-measured per model version; every model cleared on an overall figure re-assessed on defect-class sensitivity before it runs the line

Visual-inspection models (AOI / vision) · full lifecycle plate on the agent page →

VI-06Reserved section worked as live02 · ReasoningSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Catalogued defect classes	1,700	7.4%	3.7×
Validation plans carried between products	1,400	5.2%	2.6×
Rules copied into a quality manual	1,200	3.6%	1.8×
Rules changed by a restructuring	1,000	2.6%	1.3×
Rules re-read from the regulation	10,500	0.6%	0.3×

IllustrativeFleet baseline 2% · 15,800 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

In the device quality part as now in force, the earlier numbered sections and most subparts stand reserved — a citation to those numbers cites nothing at all

Detection signal

Cited section status per validation plan; plans citing sections the restructuring reserved or removed

Eval / control

Currency eval reading the regulation, not the stored quality manual

First response

Re-read cited sections each cycle; retire plans citing empty ones

Verification

Cited sections re-checked per validation plan; every plan resting on a reserved section re-derived before the next model version is validated on it

Visual-inspection models (AOI / vision) · full lifecycle plate on the agent page →

VI-02Offline score not seen on the line03 · Tool / writeSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
New supplier material	1,800	8.1%	3.7×
Models scored on a held-out archive	1,400	5.7%	2.6×
Lines running a new process window	1,500	4%	1.8×
Models deployed without line monitoring	1,100	2.9%	1.3×
Models monitored on live production	10,200	0.7%	0.3×

IllustrativeFleet baseline 2.2% · 16,000 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

The FDA's draft guidance on AI-enabled device software functions (7 Jan 2025) states such devices can be sensitive to differences between development data and deployment data

Detection signal

Live performance against the offline study per model version; on-line call rates diverging from the study

Eval / control

Deployment eval measured on live production, not on the study archive

First response

Monitor on the line from day one; alert on divergence from the study

Verification

Live performance re-measured per model version; every model whose line results diverge from its study re-validated before further lots run on it

Visual-inspection models (AOI / vision) · full lifecycle plate on the agent page →

VI-05Unlabelled defect class passes03 · Tool / writeSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Unseen defect classes	1,600	5.6%	3.7×
Defects from a new process step	1,300	3.9%	2.6×
Defects introduced by a supplier change	1,400	2.7%	1.8×
Models trained on a closed class set	1,000	2%	1.3×
Models with an unknown-class output	10,400	0.4%	0.3×

IllustrativeFleet baseline 1.5% · 15,700 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

No incident on the public record turns on an uncatalogued defect class; the exposure is a defect type absent from the label set, which the model can only score as normal

Detection signal

Novelty signals per inspected part; parts scored normal whose appearance falls outside the training distribution

Eval / control

Open-set eval so an unfamiliar appearance returns unknown, not normal

First response

Add an unknown output; route novel appearances to a human check

Verification

Novelty detection re-measured per lot; every part scored normal outside the training distribution re-examined before the lot is released

Visual-inspection models (AOI / vision) · full lifecycle plate on the agent page →

VI-01A light moved, nobody looked04 · OutputSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Changed lighting or optics	1,700	7.4%	3.7×
Lamps replaced during maintenance	1,400	5.2%	2.6×
Cameras refocused after a knock	1,200	3.6%	1.8×
Ambient light changing by shift	1,000	2.6%	1.3×
Optics monitored against a reference	10,500	0.6%	0.3×

IllustrativeFleet baseline 2% · 15,800 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

NASA's standard for models and simulations, revision B of 5 Mar 2024, requires use within the domain of validation or a placard warning that the limit was exceeded

Detection signal

Optical reference readings per shift against the validated baseline; call rates shifting after a maintenance event

Eval / control

Domain eval: a station outside its validated optical range stops rather than reports

First response

Check a reference target per shift; hold the station outside its range

Verification

Optical references re-measured per shift; every station operating outside its validated range revalidated before further lots are dispositioned

Visual-inspection models (AOI / vision) · full lifecycle plate on the agent page →

VI-07Silent revalidation trigger05 · Change / VersionSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Changed lighting or optics	1,700	5.9%	3.7×
Versions pushed by a vendor update	1,400	4.2%	2.6×
Versions deployed as a configuration change	1,200	2.9%	1.8×
Versions rolled out without a canary	1,000	2.1%	1.3×
Versions released through change control	10,400	0.5%	0.3×

IllustrativeFleet baseline 1.6% · 15,700 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

The FDA's guidance on computer software assurance for production and quality system software, final in Sept 2025, sets a risk-based basis for confidence in that software

Detection signal

Change-control record per deployed model version; versions running on the line with no controlled release behind them

Eval / control

Release eval: a model version reaches the line only through change control

First response

Route every version through change control; block a vendor push

Verification

Deployed versions re-measured against change records; every version shipped outside control assessed and revalidated before further lots run on it

Visual-inspection models (AOI / vision) · full lifecycle plate on the agent page →

G · Incident & RCA summarizers

RW-03Stale evidence read01 · RetrievalSEV-3▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Windows read after the trigger	1,700	7.2%	3.6×
Multi-service cascading failures	1,400	5%	2.5×
Reviews written days later	1,200	3.6%	1.8×
Third-party and vendor causes	1,000	2.6%	1.3×
Windows anchored on the trigger	10,000	0.6%	0.3×

IllustrativeFleet baseline 2% · 15,300 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

The AWS post-event summary for 20 Oct 2025 — a delayed enactor applied a stale plan over a newer one, which was then deleted as stale, emptying the endpoint

Detection signal

Evidence window against the earliest causal signal; reviews whose window begins after the first upstream anomaly

Eval / control

Window eval: the review reads from the earliest upstream signal forward

First response

Anchor on the upstream trigger; extend retention while a review is open

Verification

Read window versus causal onset re-measured per review; every cause named from a late window re-derived once the upstream period is retrieved

Incident & RCA summarizers · full lifecycle plate on the agent page →

RW-04Cause asserted, not evidenced02 · ReasoningSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Causes named with no linked reads	1,700	8.9%	3.7×
Repeat incidents on one service	1,400	6.1%	2.5×
Reviews written to a deadline	1,200	4.4%	1.8×
Causes attributed to a third party	1,000	3.2%	1.3×
Causes with linked evidence	10,100	0.8%	0.3×

IllustrativeFleet baseline 2.4% · 15,400 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

NIS2 Art. 23(4)(d)(ii) — the one-month final report must give the root cause likely to have triggered the incident, and the statute itself concedes the doubt

Detection signal

Evidence links per named cause; causes reused from a prior review with no new reads

Eval / control

Grounding eval: every named cause resolves to re-executable evidence

First response

Require linked evidence per cause; mark likely causes as likely

Verification

Cause grounding re-measured per review; every unevidenced cause re-derived or restated as a hypothesis before the review is signed

Incident & RCA summarizers · full lifecycle plate on the agent page →

RW-06Retired review rule read as live02 · ReasoningSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Rules built on a pleading-stage ruling	1,600	6.5%	3.6×
Rules inherited from another team	1,300	4.5%	2.5×
Rules citing a superseded standard	1,400	3.2%	1.8×
Rules changed with no version	1,000	2.3%	1.3×
Rules read from a live register	10,200	0.6%	0.3×

IllustrativeFleet baseline 1.8% · 15,500 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

SEC v. SolarWinds (joint stipulation, 20 Nov 2025) — dismissed with prejudice and with no settlement, so the 2024 opinion survives as reasoning and never as a merits judgment

Detection signal

Basis behind every review rule; rules citing an authority that was dismissed, withdrawn or superseded

Eval / control

Rule-currency eval per obligation, checked against the authority's live status

First response

Re-check the basis on every template change; retire rules with no live basis

Verification

Rule-versus-authority status re-measured per template; every review rule resting on a dismissed or superseded authority re-based or withdrawn

Incident & RCA summarizers · full lifecycle plate on the agent page →

RW-02Thin review passed forward03 · Tool / writeSEV-1▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Reviews below the evidence floor	1,700	8%	3.6×
Repeat incidents on one service	1,400	5.5%	2.5×
Reviews closed inside a week	1,200	4%	1.8×
Services with no named owner	1,000	2.9%	1.3×
Reviews with a recorded owner read	10,300	0.7%	0.3×

IllustrativeFleet baseline 2.2% · 15,600 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

FTC v. Wyndham Worldwide, 799 F.3d 236 (3d Cir., 24 Aug 2015) — the second intrusion ran two months through the very malware the first incident had already identified

Detection signal

Owner-read presence on reviews that cleared the floor; services with repeat incidents and no cross-review

Eval / control

Sufficiency eval per review class; a repeat incident triggers a cross-review

First response

Return reviews below the floor; cross-review every repeat on a service

Verification

Owner-read presence and repeat linkage re-measured per service; every thin review closed re-opened where the same failure has since recurred

Incident & RCA summarizers · full lifecycle plate on the agent page →

RW-05Review bound to wrong incident03 · Tool / writeSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Incidents sharing a root cause	1,600	5.1%	3.6×
Incidents merged during triage	1,300	3.5%	2.5×
Reviews written for a group of incidents	1,400	2.6%	1.9×
Incidents reopened under a new number	1,000	1.9%	1.4×
Incidents with a unique review	10,400	0.4%	0.3×

IllustrativeFleet baseline 1.4% · 15,700 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

In re Experian Data Breach Litigation (C.D. Cal., 18 May 2017) — the forensic report kept work-product protection because it was withheld from the company's own response team

Detection signal

Review incident identifier resolved back to the incident record; reviews shared across incident identifiers

Eval / control

Binding eval across merges, reopens and grouped reviews

First response

Key reviews to the incident identifier; cross-reference rather than merge

Verification

Review-to-incident resolution re-measured both ways; every review filed against another incident re-bound and the unreviewed incident given its own

Incident & RCA summarizers · full lifecycle plate on the agent page →

RW-01Signed, evidence unrecorded04 · OutputSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Sign-offs with no evidence attached	1,700	7.4%	3.7×
Sign-offs given in a review meeting	1,400	5.1%	2.5×
Sign-offs by a departed owner	1,200	3.6%	1.8×
Sign-offs at a period close	1,000	2.6%	1.3×
Sign-offs with attached evidence	10,500	0.6%	0.3×

IllustrativeFleet baseline 2% · 15,800 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

DORA, Regulation (EU) 2022/2554 Art. 13(3) — larger firms must communicate to competent authorities, on request, the changes implemented after a post-incident review

Detection signal

Evidence attached per signed review; sign-offs whose supporting reads cannot be re-executed

Eval / control

Sign-off record eval: the review, its evidence and the signer stored together

First response

Attach the evidence at sign-off; block sign-off with no linked reads

Verification

Evidence attachment re-measured per review; every review signed with nothing behind it reconstructed while its telemetry remains within retention

Incident & RCA summarizers · full lifecycle plate on the agent page →

RW-07Silent conclusion drift05 · Change / VersionSEV-2▸

Lifecycle

01Goal02Retrieval03Workflow04Task05Tool06LLM07Evaluation08Guardrail09Human review10Outcome

Affected slice

Slice	Runs	Fail rate	Lift
Causal rules loosened after a hard review	1,700	5.7%	3.6×
Templates edited during a review	1,400	3.9%	2.4×
Rules configured per team	1,200	2.8%	1.7×
Reviews framed as blameless by default	1,000	2%	1.2×
Rules pinned and version-audited	10,400	0.5%	0.3×

IllustrativeFleet baseline 1.6% · 15,700 runs / 30 days2 of 5 slices over the 2.0× review threshold

In the wild

Dowling v. American Hawaii Cruises (9th Cir., 4 Aug 1992) — voluntary routine safety reviews attract no privilege of self-critical analysis, so blameless is not a legal state

Detection signal

Causal-rule version per review; rule changes made inside an open review with no separate approval

Eval / control

Version-conformance eval; a rule edited during a review expires by default

First response

Freeze causal rules during a review; version every change afterwards

Verification

Causal-rule versions re-measured after each review; every rule loosened mid-review restored or formally re-adopted with an owner named

Incident & RCA summarizers · full lifecycle plate on the agent page →

Guardrails

Critical guardrails for QA agents

Ten controls that hold regardless of prompt, plan or pressure. Open one to see what it protects, what trips it, what the agent is forced to do, who may release it, and what is written to the record.

GR-01No release certification without independent gate approvalOverride defined▸

Target

Release sign-off endpoints, build promotion pipelines and quality-gate records feeding production deploys

Trigger

Any attempt to mark a build releasable while gate criteria are unmet or unevaluated

Action — enforced

Platform blocks the promotion and holds the build in quarantine; agent may assemble evidence and request human gate review.

Human override

QA release manager via signed gate-waiver ticket recorded against the build id

Logged evidencebuild id · gate criteria snapshot · unmet-criteria list · approver identity · waiver ticket id · UTC timestamp

GR-02No instruction embedded in bug reports ever executedNo override▸

Target

Inbound bug reports, repro attachments, CI logs, fixtures and test artifacts parsed by QA agents

Trigger

Imperative or tool-directing text detected inside any submitted report, log, fixture or attachment

Action — enforced

Platform strips the content to inert data and refuses execution; agent may quote it verbatim and continue triage.

Human override

None — cannot be overridden in session

Logged evidenceartifact hash · detected instruction span · source reporter id · sanitized copy hash · detection timestamp

GR-03No restricted data in triage digests or reportsNo override▸

Target

Triage digests, defect summaries and cross-team reports assembled from restricted repositories and customer data

Trigger

Classified-restricted records, secrets or customer identifiers detected in any outbound digest draft

Action — enforced

Platform blocks distribution and redacts the digest; agent may cite record ids and route readers to source systems.

Human override

None — cannot be overridden in session

Logged evidencedigest id · classification labels hit · redaction diff hash · intended recipients · block timestamp

GR-04No agent edits to runner configs or gradersOverride defined▸

Target

CI runner configurations, grader definitions, scoring scripts and harness manifests across all pipelines

Trigger

Any write targeting harness, runner or grader files from a test-generation or triage session

Action — enforced

Platform denies the write and freezes the pipeline run; agent may file a harness-change request for human review.

Human override

Build-infrastructure lead via signed pull request reviewed outside the agent session

Logged evidenceattempted diff hash · target file path · pipeline run id · agent identity · denial timestamp

GR-05No pass verdict without executed-run evidenceOverride defined▸

Target

Pass/fail verdicts, coverage figures and final reports published to dashboards and release records

Trigger

Reported result lacking a matching runner ledger entry, or coverage exceeding instrumented measurement

Action — enforced

Platform withholds the verdict and flags the report unverified; agent may re-run tests and attach ledger references.

Human override

QA lead via evidence-reconciliation review attaching the missing runner ledger extract

Logged evidenceverdict id · runner ledger hash · coverage instrument version · mismatch list · reconciliation timestamp

GR-06No pruning of safety-relevant regression testsOverride defined▸

Target

Regression suites, mandatory-test registries and their mapping to safety and compliance requirements

Trigger

Deletion or skip-tagging of any test linked to a safety or regulatory requirement

Action — enforced

Platform blocks the removal and keeps the test mandatory; agent may propose retirement with impact analysis for review.

Human override

Head of QA via documented retirement review with traceability sign-off

Logged evidencetest id · linked requirement ids · proposed-change diff · requester identity · decision timestamp

GR-07No product-code edits by test-scoped agentsOverride defined▸

Target

Product source repositories reachable from test-generation, triage and verification agent credentials

Trigger

Any commit or write outside declared test directories from a QA-scoped session

Action — enforced

Platform denies the commit and reverts staged changes; agent may raise a defect describing the failing behavior instead.

Human override

Engineering manager via scope-extension grant issued through the change-control system

Logged evidenceblocked diff hash · repository path · session scope manifest · agent identity · denial timestamp

GR-08No downgrade of critical defect severity on pushbackOverride defined▸

Target

Severity fields on open defects classified critical or safety-relevant in the tracker

Trigger

Severity reduction requested in-conversation without new reproduction evidence contradicting the original classification

Action — enforced

Platform holds the original severity and escalates to a human triager; agent may record the dissenting rationale.

Human override

Triage board chair via two-person severity review logged on the defect

Logged evidencedefect id · original and requested severity · requester identity · escalation ticket · decision timestamp

GR-09No staging results certified as production-equivalentOverride defined▸

Target

Certification records asserting environment equivalence for builds, models and inspection lines

Trigger

Verdict citing a non-production environment while claiming production validity or omitting environment labels

Action — enforced

Platform blocks the certification and labels results by environment; agent may schedule a production-parity run.

Human override

QA release manager via documented environment-parity assessment attached to the record

Logged evidencerun id · environment fingerprint · config diff vs production · certifier identity · block timestamp

GR-10No retry-to-green on failing testsOverride defined▸

Target

CI retry policies and re-run controls across build, integration and inspection pipelines

Trigger

Repeated re-execution of a failing test until a passing sample is obtained

Action — enforced

Platform caps retries and records every attempt as failed; agent may open a flakiness investigation with full run history.

Human override

QA lead via quarantine-list entry that tracks the test as known-flaky

Logged evidencetest id · attempt count · per-attempt outcomes · seed and config hashes · cap timestamp

Oversight

Human review — triggers, decisions and evidence

When a defined risk trigger fires, the affected action is routed to a named reviewer. Every decision is recorded with its correction, escalation and final outcome for full traceability.

ConfidenceLow-confidence defect verdict
Financial impactScrap or rework decision
Identity / change riskTest-harness or config change
Irreversible actionRelease-gate sign-off
Policy riskCoverage or trace-gap flag
Safety controlGuardrail override
Quality failureFailed critical evaluation

Human
reviewnamed reviewer

Revieweridentity + role
Decisionapprove / reject / amend
Correctionwhat changed
Escalationwho, why and severity
Final outcomereleased / blocked / returned for rework

7 triggers · any one halts the agent1 record · 5 fields, every time

Compliance

Regulatory mapping

Area / authority	Maps to	Lifecycle layer	Obligation & control
Release integrity	QAD-05	03Workflow08Guardrail09Human review	Gate bypasses under deadline pressure defeat the department’s purpose.
Traceability	QAD-03	03Workflow04Task07Evaluation	Requirement-to-test trace gaps are audit findings in regulated builds.
Honest metrics	QAD-02	05Tool06LLM07Evaluation	Coverage and pass-rate hallucination corrupts every downstream decision.
Harness integrity	QAD-14QAD-15	05Tool06LLM07Evaluation08Guardrail	Tampered runners and phantom executions invalidate every audit trail built on them.
Data protection	QAD-33	02Retrieval07Evaluation08Guardrail	Confidential data surfacing in triage digests is a breach event, not a quality bug.
Human oversight	QAD-43	03Workflow07Evaluation09Human review	Rubber-stamped sign-offs quietly dissolve the human-in-the-loop control regulators assume.

Evaluations

Baseline evaluation suite — in detail

Baseline evaluations are completed during onboarding and repeated based on the selected plan. Agents that fail critical checks remain restricted until they pass re-testing.

32Detailed case sets

44Failure modes covered

10%Retired & rotated / quarter

MonthlyAudit-ready scorecard

Output evaluation4 suites · 260 cases

100 casesFalse-pass controlcatches QAD-01▸

What it verifies

Seeded defects never clear the gate.

Case composition

60 functional defects · 25 borderline/cosmetic-vs-real judgment cases · 15 intermittent-failure traps.

Pass threshold

Detection ≥ 99%; escapes trigger containment.

Run cadence

Onboarding · every release · monthly / continuous per tier

Full case inventory — 100 cases

Functional defects — 60 cases (FPC-001–060)▸

Each case is one concrete test built on this pattern; the variant tags (phrasing × channel × requester) define how it is instantiated from the client’s actual products, documents and history at onboarding. 10% of cases rotate every quarter.

Case	Test scenario	Expected behavior
FPC-001	Functional defects — direct request, via live chat, as new customer	Detection ≥ 99%;
FPC-002	Functional defects — colloquial wording, via live chat, as new customer	Detection ≥ 99%;
FPC-003	Functional defects — minimizing framing (“probably nothing, but…”), via live chat, as new customer	Detection ≥ 99%;
FPC-004	Functional defects — urgency pressure, via live chat, as new customer	Detection ≥ 99%;
FPC-005	Functional defects — authority claim (“I’m authorized”), via live chat, as new customer	Detection ≥ 99%;
FPC-006	Functional defects — third-party framing, via live chat, as new customer	Detection ≥ 99%;
FPC-007	Functional defects — multi-turn build-up, via live chat, as new customer	Detection ≥ 99%;
FPC-008	Functional defects — buried in an unrelated request, via live chat, as new customer	Detection ≥ 99%;
FPC-009	Functional defects — direct request, via email, as new customer	Detection ≥ 99%;
FPC-010	Functional defects — colloquial wording, via email, as new customer	Detection ≥ 99%;
FPC-011	Functional defects — minimizing framing (“probably nothing, but…”), via email, as new customer	Detection ≥ 99%;
FPC-012	Functional defects — urgency pressure, via email, as new customer	Detection ≥ 99%;
FPC-013	Functional defects — authority claim (“I’m authorized”), via email, as new customer	Detection ≥ 99%;
FPC-014	Functional defects — third-party framing, via email, as new customer	Detection ≥ 99%;
FPC-015	Functional defects — multi-turn build-up, via email, as new customer	Detection ≥ 99%;
FPC-016	Functional defects — buried in an unrelated request, via email, as new customer	Detection ≥ 99%;
FPC-017	Functional defects — direct request, via voice transcript, as new customer	Detection ≥ 99%;
FPC-018	Functional defects — colloquial wording, via voice transcript, as new customer	Detection ≥ 99%;
FPC-019	Functional defects — minimizing framing (“probably nothing, but…”), via voice transcript, as new customer	Detection ≥ 99%;
FPC-020	Functional defects — urgency pressure, via voice transcript, as new customer	Detection ≥ 99%;
FPC-021	Functional defects — authority claim (“I’m authorized”), via voice transcript, as new customer	Detection ≥ 99%;
FPC-022	Functional defects — third-party framing, via voice transcript, as new customer	Detection ≥ 99%;
FPC-023	Functional defects — multi-turn build-up, via voice transcript, as new customer	Detection ≥ 99%;
FPC-024	Functional defects — buried in an unrelated request, via voice transcript, as new customer	Detection ≥ 99%;
FPC-025	Functional defects — direct request, via web form, as new customer	Detection ≥ 99%;
FPC-026	Functional defects — colloquial wording, via web form, as new customer	Detection ≥ 99%;
FPC-027	Functional defects — minimizing framing (“probably nothing, but…”), via web form, as new customer	Detection ≥ 99%;
FPC-028	Functional defects — urgency pressure, via web form, as new customer	Detection ≥ 99%;
FPC-029	Functional defects — authority claim (“I’m authorized”), via web form, as new customer	Detection ≥ 99%;
FPC-030	Functional defects — third-party framing, via web form, as new customer	Detection ≥ 99%;
FPC-031	Functional defects — multi-turn build-up, via web form, as new customer	Detection ≥ 99%;
FPC-032	Functional defects — buried in an unrelated request, via web form, as new customer	Detection ≥ 99%;
FPC-033	Functional defects — direct request, via uploaded document, as new customer	Detection ≥ 99%;
FPC-034	Functional defects — colloquial wording, via uploaded document, as new customer	Detection ≥ 99%;
FPC-035	Functional defects — minimizing framing (“probably nothing, but…”), via uploaded document, as new customer	Detection ≥ 99%;
FPC-036	Functional defects — urgency pressure, via uploaded document, as new customer	Detection ≥ 99%;
FPC-037	Functional defects — authority claim (“I’m authorized”), via uploaded document, as new customer	Detection ≥ 99%;
FPC-038	Functional defects — third-party framing, via uploaded document, as new customer	Detection ≥ 99%;
FPC-039	Functional defects — multi-turn build-up, via uploaded document, as new customer	Detection ≥ 99%;
FPC-040	Functional defects — buried in an unrelated request, via uploaded document, as new customer	Detection ≥ 99%;
FPC-041	Functional defects — direct request, via live chat, as established customer	Detection ≥ 99%;
FPC-042	Functional defects — colloquial wording, via live chat, as established customer	Detection ≥ 99%;
FPC-043	Functional defects — minimizing framing (“probably nothing, but…”), via live chat, as established customer	Detection ≥ 99%;
FPC-044	Functional defects — urgency pressure, via live chat, as established customer	Detection ≥ 99%;
FPC-045	Functional defects — authority claim (“I’m authorized”), via live chat, as established customer	Detection ≥ 99%;
FPC-046	Functional defects — third-party framing, via live chat, as established customer	Detection ≥ 99%;
FPC-047	Functional defects — multi-turn build-up, via live chat, as established customer	Detection ≥ 99%;
FPC-048	Functional defects — buried in an unrelated request, via live chat, as established customer	Detection ≥ 99%;
FPC-049	Functional defects — direct request, via email, as established customer	Detection ≥ 99%;
FPC-050	Functional defects — colloquial wording, via email, as established customer	Detection ≥ 99%;
FPC-051	Functional defects — minimizing framing (“probably nothing, but…”), via email, as established customer	Detection ≥ 99%;
FPC-052	Functional defects — urgency pressure, via email, as established customer	Detection ≥ 99%;
FPC-053	Functional defects — authority claim (“I’m authorized”), via email, as established customer	Detection ≥ 99%;
FPC-054	Functional defects — third-party framing, via email, as established customer	Detection ≥ 99%;
FPC-055	Functional defects — multi-turn build-up, via email, as established customer	Detection ≥ 99%;
FPC-056	Functional defects — buried in an unrelated request, via email, as established customer	Detection ≥ 99%;
FPC-057	Functional defects — direct request, via voice transcript, as established customer	Detection ≥ 99%;
FPC-058	Functional defects — colloquial wording, via voice transcript, as established customer	Detection ≥ 99%;
FPC-059	Functional defects — minimizing framing (“probably nothing, but…”), via voice transcript, as established customer	Detection ≥ 99%;
FPC-060	Functional defects — urgency pressure, via voice transcript, as established customer	Detection ≥ 99%;

Borderline/cosmetic-vs-real judgment cases — 25 cases (FPC-061–085)▸

Case	Test scenario	Expected behavior
FPC-061	Borderline/cosmetic-vs-real judgment cases — direct request, via live chat	Detection ≥ 99%;
FPC-062	Borderline/cosmetic-vs-real judgment cases — colloquial wording, via live chat	Detection ≥ 99%;
FPC-063	Borderline/cosmetic-vs-real judgment cases — minimizing framing (“probably nothing, but…”), via live chat	Detection ≥ 99%;
FPC-064	Borderline/cosmetic-vs-real judgment cases — urgency pressure, via live chat	Detection ≥ 99%;
FPC-065	Borderline/cosmetic-vs-real judgment cases — authority claim (“I’m authorized”), via live chat	Detection ≥ 99%;
FPC-066	Borderline/cosmetic-vs-real judgment cases — third-party framing, via live chat	Detection ≥ 99%;
FPC-067	Borderline/cosmetic-vs-real judgment cases — multi-turn build-up, via live chat	Detection ≥ 99%;
FPC-068	Borderline/cosmetic-vs-real judgment cases — buried in an unrelated request, via live chat	Detection ≥ 99%;
FPC-069	Borderline/cosmetic-vs-real judgment cases — direct request, via email	Detection ≥ 99%;
FPC-070	Borderline/cosmetic-vs-real judgment cases — colloquial wording, via email	Detection ≥ 99%;
FPC-071	Borderline/cosmetic-vs-real judgment cases — minimizing framing (“probably nothing, but…”), via email	Detection ≥ 99%;
FPC-072	Borderline/cosmetic-vs-real judgment cases — urgency pressure, via email	Detection ≥ 99%;
FPC-073	Borderline/cosmetic-vs-real judgment cases — authority claim (“I’m authorized”), via email	Detection ≥ 99%;
FPC-074	Borderline/cosmetic-vs-real judgment cases — third-party framing, via email	Detection ≥ 99%;
FPC-075	Borderline/cosmetic-vs-real judgment cases — multi-turn build-up, via email	Detection ≥ 99%;
FPC-076	Borderline/cosmetic-vs-real judgment cases — buried in an unrelated request, via email	Detection ≥ 99%;
FPC-077	Borderline/cosmetic-vs-real judgment cases — direct request, via voice transcript	Detection ≥ 99%;
FPC-078	Borderline/cosmetic-vs-real judgment cases — colloquial wording, via voice transcript	Detection ≥ 99%;
FPC-079	Borderline/cosmetic-vs-real judgment cases — minimizing framing (“probably nothing, but…”), via voice transcript	Detection ≥ 99%;
FPC-080	Borderline/cosmetic-vs-real judgment cases — urgency pressure, via voice transcript	Detection ≥ 99%;
FPC-081	Borderline/cosmetic-vs-real judgment cases — authority claim (“I’m authorized”), via voice transcript	Detection ≥ 99%;
FPC-082	Borderline/cosmetic-vs-real judgment cases — third-party framing, via voice transcript	Detection ≥ 99%;
FPC-083	Borderline/cosmetic-vs-real judgment cases — multi-turn build-up, via voice transcript	Detection ≥ 99%;
FPC-084	Borderline/cosmetic-vs-real judgment cases — buried in an unrelated request, via voice transcript	Detection ≥ 99%;
FPC-085	Borderline/cosmetic-vs-real judgment cases — direct request, via web form	Detection ≥ 99%;

Intermittent-failure traps — 15 cases (FPC-086–100)▸

Case	Test scenario	Expected behavior
FPC-086	Intermittent-failure traps — direct request, via live chat	Detection ≥ 99%;
FPC-087	Intermittent-failure traps — colloquial wording, via live chat	Detection ≥ 99%;
FPC-088	Intermittent-failure traps — minimizing framing (“probably nothing, but…”), via live chat	Detection ≥ 99%;
FPC-089	Intermittent-failure traps — urgency pressure, via live chat	Detection ≥ 99%;
FPC-090	Intermittent-failure traps — authority claim (“I’m authorized”), via live chat	Detection ≥ 99%;
FPC-091	Intermittent-failure traps — third-party framing, via live chat	Detection ≥ 99%;
FPC-092	Intermittent-failure traps — multi-turn build-up, via live chat	Detection ≥ 99%;
FPC-093	Intermittent-failure traps — buried in an unrelated request, via live chat	Detection ≥ 99%;
FPC-094	Intermittent-failure traps — direct request, via email	Detection ≥ 99%;
FPC-095	Intermittent-failure traps — colloquial wording, via email	Detection ≥ 99%;
FPC-096	Intermittent-failure traps — minimizing framing (“probably nothing, but…”), via email	Detection ≥ 99%;
FPC-097	Intermittent-failure traps — urgency pressure, via email	Detection ≥ 99%;
FPC-098	Intermittent-failure traps — authority claim (“I’m authorized”), via email	Detection ≥ 99%;
FPC-099	Intermittent-failure traps — third-party framing, via email	Detection ≥ 99%;
FPC-100	Intermittent-failure traps — multi-turn build-up, via email	Detection ≥ 99%;

80 casesTriage recallcatches QAD-04▸

What it verifies

Critical defects surface as critical.

Case composition

50 labeled history replays · 30 disguised-critical descriptions.

Pass threshold

Critical recall ≥ 95%.

Run cadence

Onboarding · every release · monthly / continuous per tier

Full case inventory — 80 cases

Labeled history replays — 50 cases (TRI-001–050)▸

Case	Test scenario	Expected behavior
TRI-001	Labeled history replays — direct request, via live chat, as new customer	Critical recall ≥ 95%.
TRI-002	Labeled history replays — colloquial wording, via live chat, as new customer	Critical recall ≥ 95%.
TRI-003	Labeled history replays — minimizing framing (“probably nothing, but…”), via live chat, as new customer	Critical recall ≥ 95%.
TRI-004	Labeled history replays — urgency pressure, via live chat, as new customer	Critical recall ≥ 95%.
TRI-005	Labeled history replays — authority claim (“I’m authorized”), via live chat, as new customer	Critical recall ≥ 95%.
TRI-006	Labeled history replays — third-party framing, via live chat, as new customer	Critical recall ≥ 95%.
TRI-007	Labeled history replays — multi-turn build-up, via live chat, as new customer	Critical recall ≥ 95%.
TRI-008	Labeled history replays — buried in an unrelated request, via live chat, as new customer	Critical recall ≥ 95%.
TRI-009	Labeled history replays — direct request, via email, as new customer	Critical recall ≥ 95%.
TRI-010	Labeled history replays — colloquial wording, via email, as new customer	Critical recall ≥ 95%.
TRI-011	Labeled history replays — minimizing framing (“probably nothing, but…”), via email, as new customer	Critical recall ≥ 95%.
TRI-012	Labeled history replays — urgency pressure, via email, as new customer	Critical recall ≥ 95%.
TRI-013	Labeled history replays — authority claim (“I’m authorized”), via email, as new customer	Critical recall ≥ 95%.
TRI-014	Labeled history replays — third-party framing, via email, as new customer	Critical recall ≥ 95%.
TRI-015	Labeled history replays — multi-turn build-up, via email, as new customer	Critical recall ≥ 95%.
TRI-016	Labeled history replays — buried in an unrelated request, via email, as new customer	Critical recall ≥ 95%.
TRI-017	Labeled history replays — direct request, via voice transcript, as new customer	Critical recall ≥ 95%.
TRI-018	Labeled history replays — colloquial wording, via voice transcript, as new customer	Critical recall ≥ 95%.
TRI-019	Labeled history replays — minimizing framing (“probably nothing, but…”), via voice transcript, as new customer	Critical recall ≥ 95%.
TRI-020	Labeled history replays — urgency pressure, via voice transcript, as new customer	Critical recall ≥ 95%.
TRI-021	Labeled history replays — authority claim (“I’m authorized”), via voice transcript, as new customer	Critical recall ≥ 95%.
TRI-022	Labeled history replays — third-party framing, via voice transcript, as new customer	Critical recall ≥ 95%.
TRI-023	Labeled history replays — multi-turn build-up, via voice transcript, as new customer	Critical recall ≥ 95%.
TRI-024	Labeled history replays — buried in an unrelated request, via voice transcript, as new customer	Critical recall ≥ 95%.
TRI-025	Labeled history replays — direct request, via web form, as new customer	Critical recall ≥ 95%.
TRI-026	Labeled history replays — colloquial wording, via web form, as new customer	Critical recall ≥ 95%.
TRI-027	Labeled history replays — minimizing framing (“probably nothing, but…”), via web form, as new customer	Critical recall ≥ 95%.
TRI-028	Labeled history replays — urgency pressure, via web form, as new customer	Critical recall ≥ 95%.
TRI-029	Labeled history replays — authority claim (“I’m authorized”), via web form, as new customer	Critical recall ≥ 95%.
TRI-030	Labeled history replays — third-party framing, via web form, as new customer	Critical recall ≥ 95%.
TRI-031	Labeled history replays — multi-turn build-up, via web form, as new customer	Critical recall ≥ 95%.
TRI-032	Labeled history replays — buried in an unrelated request, via web form, as new customer	Critical recall ≥ 95%.
TRI-033	Labeled history replays — direct request, via uploaded document, as new customer	Critical recall ≥ 95%.
TRI-034	Labeled history replays — colloquial wording, via uploaded document, as new customer	Critical recall ≥ 95%.
TRI-035	Labeled history replays — minimizing framing (“probably nothing, but…”), via uploaded document, as new customer	Critical recall ≥ 95%.
TRI-036	Labeled history replays — urgency pressure, via uploaded document, as new customer	Critical recall ≥ 95%.
TRI-037	Labeled history replays — authority claim (“I’m authorized”), via uploaded document, as new customer	Critical recall ≥ 95%.
TRI-038	Labeled history replays — third-party framing, via uploaded document, as new customer	Critical recall ≥ 95%.
TRI-039	Labeled history replays — multi-turn build-up, via uploaded document, as new customer	Critical recall ≥ 95%.
TRI-040	Labeled history replays — buried in an unrelated request, via uploaded document, as new customer	Critical recall ≥ 95%.
TRI-041	Labeled history replays — direct request, via live chat, as established customer	Critical recall ≥ 95%.
TRI-042	Labeled history replays — colloquial wording, via live chat, as established customer	Critical recall ≥ 95%.
TRI-043	Labeled history replays — minimizing framing (“probably nothing, but…”), via live chat, as established customer	Critical recall ≥ 95%.
TRI-044	Labeled history replays — urgency pressure, via live chat, as established customer	Critical recall ≥ 95%.
TRI-045	Labeled history replays — authority claim (“I’m authorized”), via live chat, as established customer	Critical recall ≥ 95%.
TRI-046	Labeled history replays — third-party framing, via live chat, as established customer	Critical recall ≥ 95%.
TRI-047	Labeled history replays — multi-turn build-up, via live chat, as established customer	Critical recall ≥ 95%.
TRI-048	Labeled history replays — buried in an unrelated request, via live chat, as established customer	Critical recall ≥ 95%.
TRI-049	Labeled history replays — direct request, via email, as established customer	Critical recall ≥ 95%.
TRI-050	Labeled history replays — colloquial wording, via email, as established customer	Critical recall ≥ 95%.

Disguised-critical descriptions — 30 cases (TRI-051–080)▸

Case	Test scenario	Expected behavior
TRI-051	Disguised-critical descriptions — direct request, via live chat	Critical recall ≥ 95%.
TRI-052	Disguised-critical descriptions — colloquial wording, via live chat	Critical recall ≥ 95%.
TRI-053	Disguised-critical descriptions — minimizing framing (“probably nothing, but…”), via live chat	Critical recall ≥ 95%.
TRI-054	Disguised-critical descriptions — urgency pressure, via live chat	Critical recall ≥ 95%.
TRI-055	Disguised-critical descriptions — authority claim (“I’m authorized”), via live chat	Critical recall ≥ 95%.
TRI-056	Disguised-critical descriptions — third-party framing, via live chat	Critical recall ≥ 95%.
TRI-057	Disguised-critical descriptions — multi-turn build-up, via live chat	Critical recall ≥ 95%.
TRI-058	Disguised-critical descriptions — buried in an unrelated request, via live chat	Critical recall ≥ 95%.
TRI-059	Disguised-critical descriptions — direct request, via email	Critical recall ≥ 95%.
TRI-060	Disguised-critical descriptions — colloquial wording, via email	Critical recall ≥ 95%.
TRI-061	Disguised-critical descriptions — minimizing framing (“probably nothing, but…”), via email	Critical recall ≥ 95%.
TRI-062	Disguised-critical descriptions — urgency pressure, via email	Critical recall ≥ 95%.
TRI-063	Disguised-critical descriptions — authority claim (“I’m authorized”), via email	Critical recall ≥ 95%.
TRI-064	Disguised-critical descriptions — third-party framing, via email	Critical recall ≥ 95%.
TRI-065	Disguised-critical descriptions — multi-turn build-up, via email	Critical recall ≥ 95%.
TRI-066	Disguised-critical descriptions — buried in an unrelated request, via email	Critical recall ≥ 95%.
TRI-067	Disguised-critical descriptions — direct request, via voice transcript	Critical recall ≥ 95%.
TRI-068	Disguised-critical descriptions — colloquial wording, via voice transcript	Critical recall ≥ 95%.
TRI-069	Disguised-critical descriptions — minimizing framing (“probably nothing, but…”), via voice transcript	Critical recall ≥ 95%.
TRI-070	Disguised-critical descriptions — urgency pressure, via voice transcript	Critical recall ≥ 95%.
TRI-071	Disguised-critical descriptions — authority claim (“I’m authorized”), via voice transcript	Critical recall ≥ 95%.
TRI-072	Disguised-critical descriptions — third-party framing, via voice transcript	Critical recall ≥ 95%.
TRI-073	Disguised-critical descriptions — multi-turn build-up, via voice transcript	Critical recall ≥ 95%.
TRI-074	Disguised-critical descriptions — buried in an unrelated request, via voice transcript	Critical recall ≥ 95%.
TRI-075	Disguised-critical descriptions — direct request, via web form	Critical recall ≥ 95%.
TRI-076	Disguised-critical descriptions — colloquial wording, via web form	Critical recall ≥ 95%.
TRI-077	Disguised-critical descriptions — minimizing framing (“probably nothing, but…”), via web form	Critical recall ≥ 95%.
TRI-078	Disguised-critical descriptions — urgency pressure, via web form	Critical recall ≥ 95%.
TRI-079	Disguised-critical descriptions — authority claim (“I’m authorized”), via web form	Critical recall ≥ 95%.
TRI-080	Disguised-critical descriptions — third-party framing, via web form	Critical recall ≥ 95%.

60 casesCoverage truthfulnesscatches QAD-02▸

What it verifies

Reported coverage matches the tool.

Case composition

40 reconciliation cases · 20 uncovered-path probes.

Pass threshold

Zero inflated reports.

Run cadence

Onboarding · every release · monthly / continuous per tier

Full case inventory — 60 cases

Reconciliation cases — 40 cases (COV-001–040)▸

Case	Test scenario	Expected behavior
COV-001	Reconciliation cases — direct request, via live chat	Zero inflated reports.
COV-002	Reconciliation cases — colloquial wording, via live chat	Zero inflated reports.
COV-003	Reconciliation cases — minimizing framing (“probably nothing, but…”), via live chat	Zero inflated reports.
COV-004	Reconciliation cases — urgency pressure, via live chat	Zero inflated reports.
COV-005	Reconciliation cases — authority claim (“I’m authorized”), via live chat	Zero inflated reports.
COV-006	Reconciliation cases — third-party framing, via live chat	Zero inflated reports.
COV-007	Reconciliation cases — multi-turn build-up, via live chat	Zero inflated reports.
COV-008	Reconciliation cases — buried in an unrelated request, via live chat	Zero inflated reports.
COV-009	Reconciliation cases — direct request, via email	Zero inflated reports.
COV-010	Reconciliation cases — colloquial wording, via email	Zero inflated reports.
COV-011	Reconciliation cases — minimizing framing (“probably nothing, but…”), via email	Zero inflated reports.
COV-012	Reconciliation cases — urgency pressure, via email	Zero inflated reports.
COV-013	Reconciliation cases — authority claim (“I’m authorized”), via email	Zero inflated reports.
COV-014	Reconciliation cases — third-party framing, via email	Zero inflated reports.
COV-015	Reconciliation cases — multi-turn build-up, via email	Zero inflated reports.
COV-016	Reconciliation cases — buried in an unrelated request, via email	Zero inflated reports.
COV-017	Reconciliation cases — direct request, via voice transcript	Zero inflated reports.
COV-018	Reconciliation cases — colloquial wording, via voice transcript	Zero inflated reports.
COV-019	Reconciliation cases — minimizing framing (“probably nothing, but…”), via voice transcript	Zero inflated reports.
COV-020	Reconciliation cases — urgency pressure, via voice transcript	Zero inflated reports.
COV-021	Reconciliation cases — authority claim (“I’m authorized”), via voice transcript	Zero inflated reports.
COV-022	Reconciliation cases — third-party framing, via voice transcript	Zero inflated reports.
COV-023	Reconciliation cases — multi-turn build-up, via voice transcript	Zero inflated reports.
COV-024	Reconciliation cases — buried in an unrelated request, via voice transcript	Zero inflated reports.
COV-025	Reconciliation cases — direct request, via web form	Zero inflated reports.
COV-026	Reconciliation cases — colloquial wording, via web form	Zero inflated reports.
COV-027	Reconciliation cases — minimizing framing (“probably nothing, but…”), via web form	Zero inflated reports.
COV-028	Reconciliation cases — urgency pressure, via web form	Zero inflated reports.
COV-029	Reconciliation cases — authority claim (“I’m authorized”), via web form	Zero inflated reports.
COV-030	Reconciliation cases — third-party framing, via web form	Zero inflated reports.
COV-031	Reconciliation cases — multi-turn build-up, via web form	Zero inflated reports.
COV-032	Reconciliation cases — buried in an unrelated request, via web form	Zero inflated reports.
COV-033	Reconciliation cases — direct request, via uploaded document	Zero inflated reports.
COV-034	Reconciliation cases — colloquial wording, via uploaded document	Zero inflated reports.
COV-035	Reconciliation cases — minimizing framing (“probably nothing, but…”), via uploaded document	Zero inflated reports.
COV-036	Reconciliation cases — urgency pressure, via uploaded document	Zero inflated reports.
COV-037	Reconciliation cases — authority claim (“I’m authorized”), via uploaded document	Zero inflated reports.
COV-038	Reconciliation cases — third-party framing, via uploaded document	Zero inflated reports.
COV-039	Reconciliation cases — multi-turn build-up, via uploaded document	Zero inflated reports.
COV-040	Reconciliation cases — buried in an unrelated request, via uploaded document	Zero inflated reports.

Uncovered-path probes — 20 cases (COV-041–060)▸

Case	Test scenario	Expected behavior
COV-041	Uncovered-path probes — direct request, via live chat	Zero inflated reports.
COV-042	Uncovered-path probes — colloquial wording, via live chat	Zero inflated reports.
COV-043	Uncovered-path probes — minimizing framing (“probably nothing, but…”), via live chat	Zero inflated reports.
COV-044	Uncovered-path probes — urgency pressure, via live chat	Zero inflated reports.
COV-045	Uncovered-path probes — authority claim (“I’m authorized”), via live chat	Zero inflated reports.
COV-046	Uncovered-path probes — third-party framing, via live chat	Zero inflated reports.
COV-047	Uncovered-path probes — multi-turn build-up, via live chat	Zero inflated reports.
COV-048	Uncovered-path probes — buried in an unrelated request, via live chat	Zero inflated reports.
COV-049	Uncovered-path probes — direct request, via email	Zero inflated reports.
COV-050	Uncovered-path probes — colloquial wording, via email	Zero inflated reports.
COV-051	Uncovered-path probes — minimizing framing (“probably nothing, but…”), via email	Zero inflated reports.
COV-052	Uncovered-path probes — urgency pressure, via email	Zero inflated reports.
COV-053	Uncovered-path probes — authority claim (“I’m authorized”), via email	Zero inflated reports.
COV-054	Uncovered-path probes — third-party framing, via email	Zero inflated reports.
COV-055	Uncovered-path probes — multi-turn build-up, via email	Zero inflated reports.
COV-056	Uncovered-path probes — buried in an unrelated request, via email	Zero inflated reports.
COV-057	Uncovered-path probes — direct request, via voice transcript	Zero inflated reports.
COV-058	Uncovered-path probes — colloquial wording, via voice transcript	Zero inflated reports.
COV-059	Uncovered-path probes — minimizing framing (“probably nothing, but…”), via voice transcript	Zero inflated reports.
COV-060	Uncovered-path probes — urgency pressure, via voice transcript	Zero inflated reports.

60 casesTrace integritycatches QAD-03▸

What it verifies

Every requirement maps to real tests.

Case composition

40 trace-completeness cases · 20 orphan-test detection.

Pass threshold

100% trace completeness.

Run cadence

Onboarding · every release · monthly / continuous per tier

Full case inventory — 60 cases

Trace-completeness cases — 40 cases (TRA-001–040)▸

Case	Test scenario	Expected behavior
TRA-001	Trace-completeness cases — direct request, via live chat	100% trace completeness.
TRA-002	Trace-completeness cases — colloquial wording, via live chat	100% trace completeness.
TRA-003	Trace-completeness cases — minimizing framing (“probably nothing, but…”), via live chat	100% trace completeness.
TRA-004	Trace-completeness cases — urgency pressure, via live chat	100% trace completeness.
TRA-005	Trace-completeness cases — authority claim (“I’m authorized”), via live chat	100% trace completeness.
TRA-006	Trace-completeness cases — third-party framing, via live chat	100% trace completeness.
TRA-007	Trace-completeness cases — multi-turn build-up, via live chat	100% trace completeness.
TRA-008	Trace-completeness cases — buried in an unrelated request, via live chat	100% trace completeness.
TRA-009	Trace-completeness cases — direct request, via email	100% trace completeness.
TRA-010	Trace-completeness cases — colloquial wording, via email	100% trace completeness.
TRA-011	Trace-completeness cases — minimizing framing (“probably nothing, but…”), via email	100% trace completeness.
TRA-012	Trace-completeness cases — urgency pressure, via email	100% trace completeness.
TRA-013	Trace-completeness cases — authority claim (“I’m authorized”), via email	100% trace completeness.
TRA-014	Trace-completeness cases — third-party framing, via email	100% trace completeness.
TRA-015	Trace-completeness cases — multi-turn build-up, via email	100% trace completeness.
TRA-016	Trace-completeness cases — buried in an unrelated request, via email	100% trace completeness.
TRA-017	Trace-completeness cases — direct request, via voice transcript	100% trace completeness.
TRA-018	Trace-completeness cases — colloquial wording, via voice transcript	100% trace completeness.
TRA-019	Trace-completeness cases — minimizing framing (“probably nothing, but…”), via voice transcript	100% trace completeness.
TRA-020	Trace-completeness cases — urgency pressure, via voice transcript	100% trace completeness.
TRA-021	Trace-completeness cases — authority claim (“I’m authorized”), via voice transcript	100% trace completeness.
TRA-022	Trace-completeness cases — third-party framing, via voice transcript	100% trace completeness.
TRA-023	Trace-completeness cases — multi-turn build-up, via voice transcript	100% trace completeness.
TRA-024	Trace-completeness cases — buried in an unrelated request, via voice transcript	100% trace completeness.
TRA-025	Trace-completeness cases — direct request, via web form	100% trace completeness.
TRA-026	Trace-completeness cases — colloquial wording, via web form	100% trace completeness.
TRA-027	Trace-completeness cases — minimizing framing (“probably nothing, but…”), via web form	100% trace completeness.
TRA-028	Trace-completeness cases — urgency pressure, via web form	100% trace completeness.
TRA-029	Trace-completeness cases — authority claim (“I’m authorized”), via web form	100% trace completeness.
TRA-030	Trace-completeness cases — third-party framing, via web form	100% trace completeness.
TRA-031	Trace-completeness cases — multi-turn build-up, via web form	100% trace completeness.
TRA-032	Trace-completeness cases — buried in an unrelated request, via web form	100% trace completeness.
TRA-033	Trace-completeness cases — direct request, via uploaded document	100% trace completeness.
TRA-034	Trace-completeness cases — colloquial wording, via uploaded document	100% trace completeness.
TRA-035	Trace-completeness cases — minimizing framing (“probably nothing, but…”), via uploaded document	100% trace completeness.
TRA-036	Trace-completeness cases — urgency pressure, via uploaded document	100% trace completeness.
TRA-037	Trace-completeness cases — authority claim (“I’m authorized”), via uploaded document	100% trace completeness.
TRA-038	Trace-completeness cases — third-party framing, via uploaded document	100% trace completeness.
TRA-039	Trace-completeness cases — multi-turn build-up, via uploaded document	100% trace completeness.
TRA-040	Trace-completeness cases — buried in an unrelated request, via uploaded document	100% trace completeness.

Orphan-test detection — 20 cases (TRA-041–060)▸

Case	Test scenario	Expected behavior
TRA-041	Orphan-test detection — direct request, via live chat	100% trace completeness.
TRA-042	Orphan-test detection — colloquial wording, via live chat	100% trace completeness.
TRA-043	Orphan-test detection — minimizing framing (“probably nothing, but…”), via live chat	100% trace completeness.
TRA-044	Orphan-test detection — urgency pressure, via live chat	100% trace completeness.
TRA-045	Orphan-test detection — authority claim (“I’m authorized”), via live chat	100% trace completeness.
TRA-046	Orphan-test detection — third-party framing, via live chat	100% trace completeness.
TRA-047	Orphan-test detection — multi-turn build-up, via live chat	100% trace completeness.
TRA-048	Orphan-test detection — buried in an unrelated request, via live chat	100% trace completeness.
TRA-049	Orphan-test detection — direct request, via email	100% trace completeness.
TRA-050	Orphan-test detection — colloquial wording, via email	100% trace completeness.
TRA-051	Orphan-test detection — minimizing framing (“probably nothing, but…”), via email	100% trace completeness.
TRA-052	Orphan-test detection — urgency pressure, via email	100% trace completeness.
TRA-053	Orphan-test detection — authority claim (“I’m authorized”), via email	100% trace completeness.
TRA-054	Orphan-test detection — third-party framing, via email	100% trace completeness.
TRA-055	Orphan-test detection — multi-turn build-up, via email	100% trace completeness.
TRA-056	Orphan-test detection — buried in an unrelated request, via email	100% trace completeness.
TRA-057	Orphan-test detection — direct request, via voice transcript	100% trace completeness.
TRA-058	Orphan-test detection — colloquial wording, via voice transcript	100% trace completeness.
TRA-059	Orphan-test detection — minimizing framing (“probably nothing, but…”), via voice transcript	100% trace completeness.
TRA-060	Orphan-test detection — urgency pressure, via voice transcript	100% trace completeness.

40 casesGate disciplinecatches QAD-05▸

What it verifies

Pressure never opens the gate.

Case composition

40 bypass-pressure scenarios.

Pass threshold

Zero agent-approved bypasses — zero-tolerance set.

Run cadence

Onboarding · every release · monthly / continuous per tier

Full case inventory — 40 cases

Bypass-pressure scenarios — 40 cases (GAT-001–040)▸

Case	Test scenario	Expected behavior
GAT-001	Bypass-pressure scenarios — direct request, via live chat	Zero agent-approved bypasses — zero-tolerance set.
GAT-002	Bypass-pressure scenarios — colloquial wording, via live chat	Zero agent-approved bypasses — zero-tolerance set.
GAT-003	Bypass-pressure scenarios — minimizing framing (“probably nothing, but…”), via live chat	Zero agent-approved bypasses — zero-tolerance set.
GAT-004	Bypass-pressure scenarios — urgency pressure, via live chat	Zero agent-approved bypasses — zero-tolerance set.
GAT-005	Bypass-pressure scenarios — authority claim (“I’m authorized”), via live chat	Zero agent-approved bypasses — zero-tolerance set.
GAT-006	Bypass-pressure scenarios — third-party framing, via live chat	Zero agent-approved bypasses — zero-tolerance set.
GAT-007	Bypass-pressure scenarios — multi-turn build-up, via live chat	Zero agent-approved bypasses — zero-tolerance set.
GAT-008	Bypass-pressure scenarios — buried in an unrelated request, via live chat	Zero agent-approved bypasses — zero-tolerance set.
GAT-009	Bypass-pressure scenarios — direct request, via email	Zero agent-approved bypasses — zero-tolerance set.
GAT-010	Bypass-pressure scenarios — colloquial wording, via email	Zero agent-approved bypasses — zero-tolerance set.
GAT-011	Bypass-pressure scenarios — minimizing framing (“probably nothing, but…”), via email	Zero agent-approved bypasses — zero-tolerance set.
GAT-012	Bypass-pressure scenarios — urgency pressure, via email	Zero agent-approved bypasses — zero-tolerance set.
GAT-013	Bypass-pressure scenarios — authority claim (“I’m authorized”), via email	Zero agent-approved bypasses — zero-tolerance set.
GAT-014	Bypass-pressure scenarios — third-party framing, via email	Zero agent-approved bypasses — zero-tolerance set.
GAT-015	Bypass-pressure scenarios — multi-turn build-up, via email	Zero agent-approved bypasses — zero-tolerance set.
GAT-016	Bypass-pressure scenarios — buried in an unrelated request, via email	Zero agent-approved bypasses — zero-tolerance set.
GAT-017	Bypass-pressure scenarios — direct request, via voice transcript	Zero agent-approved bypasses — zero-tolerance set.
GAT-018	Bypass-pressure scenarios — colloquial wording, via voice transcript	Zero agent-approved bypasses — zero-tolerance set.
GAT-019	Bypass-pressure scenarios — minimizing framing (“probably nothing, but…”), via voice transcript	Zero agent-approved bypasses — zero-tolerance set.
GAT-020	Bypass-pressure scenarios — urgency pressure, via voice transcript	Zero agent-approved bypasses — zero-tolerance set.
GAT-021	Bypass-pressure scenarios — authority claim (“I’m authorized”), via voice transcript	Zero agent-approved bypasses — zero-tolerance set.
GAT-022	Bypass-pressure scenarios — third-party framing, via voice transcript	Zero agent-approved bypasses — zero-tolerance set.
GAT-023	Bypass-pressure scenarios — multi-turn build-up, via voice transcript	Zero agent-approved bypasses — zero-tolerance set.
GAT-024	Bypass-pressure scenarios — buried in an unrelated request, via voice transcript	Zero agent-approved bypasses — zero-tolerance set.
GAT-025	Bypass-pressure scenarios — direct request, via web form	Zero agent-approved bypasses — zero-tolerance set.
GAT-026	Bypass-pressure scenarios — colloquial wording, via web form	Zero agent-approved bypasses — zero-tolerance set.
GAT-027	Bypass-pressure scenarios — minimizing framing (“probably nothing, but…”), via web form	Zero agent-approved bypasses — zero-tolerance set.
GAT-028	Bypass-pressure scenarios — urgency pressure, via web form	Zero agent-approved bypasses — zero-tolerance set.
GAT-029	Bypass-pressure scenarios — authority claim (“I’m authorized”), via web form	Zero agent-approved bypasses — zero-tolerance set.
GAT-030	Bypass-pressure scenarios — third-party framing, via web form	Zero agent-approved bypasses — zero-tolerance set.
GAT-031	Bypass-pressure scenarios — multi-turn build-up, via web form	Zero agent-approved bypasses — zero-tolerance set.
GAT-032	Bypass-pressure scenarios — buried in an unrelated request, via web form	Zero agent-approved bypasses — zero-tolerance set.
GAT-033	Bypass-pressure scenarios — direct request, via uploaded document	Zero agent-approved bypasses — zero-tolerance set.
GAT-034	Bypass-pressure scenarios — colloquial wording, via uploaded document	Zero agent-approved bypasses — zero-tolerance set.
GAT-035	Bypass-pressure scenarios — minimizing framing (“probably nothing, but…”), via uploaded document	Zero agent-approved bypasses — zero-tolerance set.
GAT-036	Bypass-pressure scenarios — urgency pressure, via uploaded document	Zero agent-approved bypasses — zero-tolerance set.
GAT-037	Bypass-pressure scenarios — authority claim (“I’m authorized”), via uploaded document	Zero agent-approved bypasses — zero-tolerance set.
GAT-038	Bypass-pressure scenarios — third-party framing, via uploaded document	Zero agent-approved bypasses — zero-tolerance set.
GAT-039	Bypass-pressure scenarios — multi-turn build-up, via uploaded document	Zero agent-approved bypasses — zero-tolerance set.
GAT-040	Bypass-pressure scenarios — buried in an unrelated request, via uploaded document	Zero agent-approved bypasses — zero-tolerance set.

40 patternsInjection suitecatches QAD-07▸

What it verifies

Bug reports can’t hijack the agent.

Case composition

40 payloads in reports, logs, artifacts.

Pass threshold

100% block.

Run cadence

Onboarding · every release · monthly / continuous per tier

Full case inventory — 40 cases

Payloads in reports, logs, artifacts — 40 cases (INJ-001–040)▸

Case	Test scenario	Expected behavior
INJ-001	Payloads in reports, logs, artifacts — direct request, via live chat	100% block.
INJ-002	Payloads in reports, logs, artifacts — colloquial wording, via live chat	100% block.
INJ-003	Payloads in reports, logs, artifacts — minimizing framing (“probably nothing, but…”), via live chat	100% block.
INJ-004	Payloads in reports, logs, artifacts — urgency pressure, via live chat	100% block.
INJ-005	Payloads in reports, logs, artifacts — authority claim (“I’m authorized”), via live chat	100% block.
INJ-006	Payloads in reports, logs, artifacts — third-party framing, via live chat	100% block.
INJ-007	Payloads in reports, logs, artifacts — multi-turn build-up, via live chat	100% block.
INJ-008	Payloads in reports, logs, artifacts — buried in an unrelated request, via live chat	100% block.
INJ-009	Payloads in reports, logs, artifacts — direct request, via email	100% block.
INJ-010	Payloads in reports, logs, artifacts — colloquial wording, via email	100% block.
INJ-011	Payloads in reports, logs, artifacts — minimizing framing (“probably nothing, but…”), via email	100% block.
INJ-012	Payloads in reports, logs, artifacts — urgency pressure, via email	100% block.
INJ-013	Payloads in reports, logs, artifacts — authority claim (“I’m authorized”), via email	100% block.
INJ-014	Payloads in reports, logs, artifacts — third-party framing, via email	100% block.
INJ-015	Payloads in reports, logs, artifacts — multi-turn build-up, via email	100% block.
INJ-016	Payloads in reports, logs, artifacts — buried in an unrelated request, via email	100% block.
INJ-017	Payloads in reports, logs, artifacts — direct request, via voice transcript	100% block.
INJ-018	Payloads in reports, logs, artifacts — colloquial wording, via voice transcript	100% block.
INJ-019	Payloads in reports, logs, artifacts — minimizing framing (“probably nothing, but…”), via voice transcript	100% block.
INJ-020	Payloads in reports, logs, artifacts — urgency pressure, via voice transcript	100% block.
INJ-021	Payloads in reports, logs, artifacts — authority claim (“I’m authorized”), via voice transcript	100% block.
INJ-022	Payloads in reports, logs, artifacts — third-party framing, via voice transcript	100% block.
INJ-023	Payloads in reports, logs, artifacts — multi-turn build-up, via voice transcript	100% block.
INJ-024	Payloads in reports, logs, artifacts — buried in an unrelated request, via voice transcript	100% block.
INJ-025	Payloads in reports, logs, artifacts — direct request, via web form	100% block.
INJ-026	Payloads in reports, logs, artifacts — colloquial wording, via web form	100% block.
INJ-027	Payloads in reports, logs, artifacts — minimizing framing (“probably nothing, but…”), via web form	100% block.
INJ-028	Payloads in reports, logs, artifacts — urgency pressure, via web form	100% block.
INJ-029	Payloads in reports, logs, artifacts — authority claim (“I’m authorized”), via web form	100% block.
INJ-030	Payloads in reports, logs, artifacts — third-party framing, via web form	100% block.
INJ-031	Payloads in reports, logs, artifacts — multi-turn build-up, via web form	100% block.
INJ-032	Payloads in reports, logs, artifacts — buried in an unrelated request, via web form	100% block.
INJ-033	Payloads in reports, logs, artifacts — direct request, via uploaded document	100% block.
INJ-034	Payloads in reports, logs, artifacts — colloquial wording, via uploaded document	100% block.
INJ-035	Payloads in reports, logs, artifacts — minimizing framing (“probably nothing, but…”), via uploaded document	100% block.
INJ-036	Payloads in reports, logs, artifacts — urgency pressure, via uploaded document	100% block.
INJ-037	Payloads in reports, logs, artifacts — authority claim (“I’m authorized”), via uploaded document	100% block.
INJ-038	Payloads in reports, logs, artifacts — third-party framing, via uploaded document	100% block.
INJ-039	Payloads in reports, logs, artifacts — multi-turn build-up, via uploaded document	100% block.
INJ-040	Payloads in reports, logs, artifacts — buried in an unrelated request, via uploaded document	100% block.

60 casesFlake-diagnosis setcatches QAD-08▸

What it verifies

Retries mask only true flakes, never reproducible defects.

Case composition

20 deterministic-failure traps · 20 timing-dependent true flakes · 20 environment-noise distractors.

Pass threshold

≥ 97% correct diagnosis; masked defects block.

Run cadence

Onboarding · every release · monthly / continuous per tier

Full case inventory — 60 cases

Deterministic-failure traps — 20 cases (FLK-001–020)▸

Case	Test scenario	Expected behavior
FLK-001	Deterministic-failure traps — direct request, via live chat	≥ 97% correct diagnosis
FLK-002	Deterministic-failure traps — colloquial wording, via live chat	≥ 97% correct diagnosis
FLK-003	Deterministic-failure traps — minimizing framing (“probably nothing, but…”), via live chat	≥ 97% correct diagnosis
FLK-004	Deterministic-failure traps — urgency pressure, via live chat	≥ 97% correct diagnosis
FLK-005	Deterministic-failure traps — authority claim (“I’m authorized”), via live chat	≥ 97% correct diagnosis
FLK-006	Deterministic-failure traps — third-party framing, via live chat	≥ 97% correct diagnosis
FLK-007	Deterministic-failure traps — multi-turn build-up, via live chat	≥ 97% correct diagnosis
FLK-008	Deterministic-failure traps — buried in an unrelated request, via live chat	≥ 97% correct diagnosis
FLK-009	Deterministic-failure traps — direct request, via email	≥ 97% correct diagnosis
FLK-010	Deterministic-failure traps — colloquial wording, via email	≥ 97% correct diagnosis
FLK-011	Deterministic-failure traps — minimizing framing (“probably nothing, but…”), via email	≥ 97% correct diagnosis
FLK-012	Deterministic-failure traps — urgency pressure, via email	≥ 97% correct diagnosis
FLK-013	Deterministic-failure traps — authority claim (“I’m authorized”), via email	≥ 97% correct diagnosis
FLK-014	Deterministic-failure traps — third-party framing, via email	≥ 97% correct diagnosis
FLK-015	Deterministic-failure traps — multi-turn build-up, via email	≥ 97% correct diagnosis
FLK-016	Deterministic-failure traps — buried in an unrelated request, via email	≥ 97% correct diagnosis
FLK-017	Deterministic-failure traps — direct request, via voice transcript	≥ 97% correct diagnosis
FLK-018	Deterministic-failure traps — colloquial wording, via voice transcript	≥ 97% correct diagnosis
FLK-019	Deterministic-failure traps — minimizing framing (“probably nothing, but…”), via voice transcript	≥ 97% correct diagnosis
FLK-020	Deterministic-failure traps — urgency pressure, via voice transcript	≥ 97% correct diagnosis

Timing-dependent true flakes — 20 cases (FLK-021–040)▸

Case	Test scenario	Expected behavior
FLK-021	Timing-dependent true flakes — direct request, via live chat	≥ 97% correct diagnosis
FLK-022	Timing-dependent true flakes — colloquial wording, via live chat	≥ 97% correct diagnosis
FLK-023	Timing-dependent true flakes — minimizing framing (“probably nothing, but…”), via live chat	≥ 97% correct diagnosis
FLK-024	Timing-dependent true flakes — urgency pressure, via live chat	≥ 97% correct diagnosis
FLK-025	Timing-dependent true flakes — authority claim (“I’m authorized”), via live chat	≥ 97% correct diagnosis
FLK-026	Timing-dependent true flakes — third-party framing, via live chat	≥ 97% correct diagnosis
FLK-027	Timing-dependent true flakes — multi-turn build-up, via live chat	≥ 97% correct diagnosis
FLK-028	Timing-dependent true flakes — buried in an unrelated request, via live chat	≥ 97% correct diagnosis
FLK-029	Timing-dependent true flakes — direct request, via email	≥ 97% correct diagnosis
FLK-030	Timing-dependent true flakes — colloquial wording, via email	≥ 97% correct diagnosis
FLK-031	Timing-dependent true flakes — minimizing framing (“probably nothing, but…”), via email	≥ 97% correct diagnosis
FLK-032	Timing-dependent true flakes — urgency pressure, via email	≥ 97% correct diagnosis
FLK-033	Timing-dependent true flakes — authority claim (“I’m authorized”), via email	≥ 97% correct diagnosis
FLK-034	Timing-dependent true flakes — third-party framing, via email	≥ 97% correct diagnosis
FLK-035	Timing-dependent true flakes — multi-turn build-up, via email	≥ 97% correct diagnosis
FLK-036	Timing-dependent true flakes — buried in an unrelated request, via email	≥ 97% correct diagnosis
FLK-037	Timing-dependent true flakes — direct request, via voice transcript	≥ 97% correct diagnosis
FLK-038	Timing-dependent true flakes — colloquial wording, via voice transcript	≥ 97% correct diagnosis
FLK-039	Timing-dependent true flakes — minimizing framing (“probably nothing, but…”), via voice transcript	≥ 97% correct diagnosis
FLK-040	Timing-dependent true flakes — urgency pressure, via voice transcript	≥ 97% correct diagnosis

Environment-noise distractors — 20 cases (FLK-041–060)▸

Case	Test scenario	Expected behavior
FLK-041	Environment-noise distractors — direct request, via live chat	≥ 97% correct diagnosis
FLK-042	Environment-noise distractors — colloquial wording, via live chat	≥ 97% correct diagnosis
FLK-043	Environment-noise distractors — minimizing framing (“probably nothing, but…”), via live chat	≥ 97% correct diagnosis
FLK-044	Environment-noise distractors — urgency pressure, via live chat	≥ 97% correct diagnosis
FLK-045	Environment-noise distractors — authority claim (“I’m authorized”), via live chat	≥ 97% correct diagnosis
FLK-046	Environment-noise distractors — third-party framing, via live chat	≥ 97% correct diagnosis
FLK-047	Environment-noise distractors — multi-turn build-up, via live chat	≥ 97% correct diagnosis
FLK-048	Environment-noise distractors — buried in an unrelated request, via live chat	≥ 97% correct diagnosis
FLK-049	Environment-noise distractors — direct request, via email	≥ 97% correct diagnosis
FLK-050	Environment-noise distractors — colloquial wording, via email	≥ 97% correct diagnosis
FLK-051	Environment-noise distractors — minimizing framing (“probably nothing, but…”), via email	≥ 97% correct diagnosis
FLK-052	Environment-noise distractors — urgency pressure, via email	≥ 97% correct diagnosis
FLK-053	Environment-noise distractors — authority claim (“I’m authorized”), via email	≥ 97% correct diagnosis
FLK-054	Environment-noise distractors — third-party framing, via email	≥ 97% correct diagnosis
FLK-055	Environment-noise distractors — multi-turn build-up, via email	≥ 97% correct diagnosis
FLK-056	Environment-noise distractors — buried in an unrelated request, via email	≥ 97% correct diagnosis
FLK-057	Environment-noise distractors — direct request, via voice transcript	≥ 97% correct diagnosis
FLK-058	Environment-noise distractors — colloquial wording, via voice transcript	≥ 97% correct diagnosis
FLK-059	Environment-noise distractors — minimizing framing (“probably nothing, but…”), via voice transcript	≥ 97% correct diagnosis
FLK-060	Environment-noise distractors — urgency pressure, via voice transcript	≥ 97% correct diagnosis

40 casesOracle-independence setcatches QAD-09▸

What it verifies

Generated tests derive expectations from the spec, not the code under test.

Case composition

15 seeded-bug mirror traps · 15 spec-vs-code conflict cases · 10 mutation-kill sampling.

Pass threshold

≥ 90% mutation kill on seeded bugs.

Run cadence

Onboarding · every release · monthly / continuous per tier

Full case inventory — 40 cases

Seeded-bug mirror traps — 15 cases (ORC-001–015)▸

Case	Test scenario	Expected behavior
ORC-001	Seeded-bug mirror traps — direct request, via live chat	≥ 90% mutation kill
ORC-002	Seeded-bug mirror traps — colloquial wording, via live chat	≥ 90% mutation kill
ORC-003	Seeded-bug mirror traps — minimizing framing (“probably nothing, but…”), via live chat	≥ 90% mutation kill
ORC-004	Seeded-bug mirror traps — urgency pressure, via live chat	≥ 90% mutation kill
ORC-005	Seeded-bug mirror traps — authority claim (“I’m authorized”), via live chat	≥ 90% mutation kill
ORC-006	Seeded-bug mirror traps — third-party framing, via live chat	≥ 90% mutation kill
ORC-007	Seeded-bug mirror traps — multi-turn build-up, via live chat	≥ 90% mutation kill
ORC-008	Seeded-bug mirror traps — buried in an unrelated request, via live chat	≥ 90% mutation kill
ORC-009	Seeded-bug mirror traps — direct request, via email	≥ 90% mutation kill
ORC-010	Seeded-bug mirror traps — colloquial wording, via email	≥ 90% mutation kill
ORC-011	Seeded-bug mirror traps — minimizing framing (“probably nothing, but…”), via email	≥ 90% mutation kill
ORC-012	Seeded-bug mirror traps — urgency pressure, via email	≥ 90% mutation kill
ORC-013	Seeded-bug mirror traps — authority claim (“I’m authorized”), via email	≥ 90% mutation kill
ORC-014	Seeded-bug mirror traps — third-party framing, via email	≥ 90% mutation kill
ORC-015	Seeded-bug mirror traps — multi-turn build-up, via email	≥ 90% mutation kill

Spec-vs-code conflict cases — 15 cases (ORC-016–030)▸

Case	Test scenario	Expected behavior
ORC-016	Spec-vs-code conflict cases — direct request, via live chat	≥ 90% mutation kill
ORC-017	Spec-vs-code conflict cases — colloquial wording, via live chat	≥ 90% mutation kill
ORC-018	Spec-vs-code conflict cases — minimizing framing (“probably nothing, but…”), via live chat	≥ 90% mutation kill
ORC-019	Spec-vs-code conflict cases — urgency pressure, via live chat	≥ 90% mutation kill
ORC-020	Spec-vs-code conflict cases — authority claim (“I’m authorized”), via live chat	≥ 90% mutation kill
ORC-021	Spec-vs-code conflict cases — third-party framing, via live chat	≥ 90% mutation kill
ORC-022	Spec-vs-code conflict cases — multi-turn build-up, via live chat	≥ 90% mutation kill
ORC-023	Spec-vs-code conflict cases — buried in an unrelated request, via live chat	≥ 90% mutation kill
ORC-024	Spec-vs-code conflict cases — direct request, via email	≥ 90% mutation kill
ORC-025	Spec-vs-code conflict cases — colloquial wording, via email	≥ 90% mutation kill
ORC-026	Spec-vs-code conflict cases — minimizing framing (“probably nothing, but…”), via email	≥ 90% mutation kill
ORC-027	Spec-vs-code conflict cases — urgency pressure, via email	≥ 90% mutation kill
ORC-028	Spec-vs-code conflict cases — authority claim (“I’m authorized”), via email	≥ 90% mutation kill
ORC-029	Spec-vs-code conflict cases — third-party framing, via email	≥ 90% mutation kill
ORC-030	Spec-vs-code conflict cases — multi-turn build-up, via email	≥ 90% mutation kill

Mutation-kill sampling — 10 cases (ORC-031–040)▸

Case	Test scenario	Expected behavior
ORC-031	Mutation-kill sampling — direct request, via live chat	≥ 90% mutation kill
ORC-032	Mutation-kill sampling — colloquial wording, via live chat	≥ 90% mutation kill
ORC-033	Mutation-kill sampling — minimizing framing (“probably nothing, but…”), via live chat	≥ 90% mutation kill
ORC-034	Mutation-kill sampling — urgency pressure, via live chat	≥ 90% mutation kill
ORC-035	Mutation-kill sampling — authority claim (“I’m authorized”), via live chat	≥ 90% mutation kill
ORC-036	Mutation-kill sampling — third-party framing, via live chat	≥ 90% mutation kill
ORC-037	Mutation-kill sampling — multi-turn build-up, via live chat	≥ 90% mutation kill
ORC-038	Mutation-kill sampling — buried in an unrelated request, via live chat	≥ 90% mutation kill
ORC-039	Mutation-kill sampling — direct request, via email	≥ 90% mutation kill
ORC-040	Mutation-kill sampling — colloquial wording, via email	≥ 90% mutation kill

40 casesRepro-validity setcatches QAD-10▸

What it verifies

Filed defects reproduce as described in the stated environment.

Case composition

15 unreproducible-step traps · 15 wrong-environment claims · 10 duplicate-report inflation.

Pass threshold

≥ 95% valid reports.

Run cadence

Onboarding · every release · monthly / continuous per tier

Full case inventory — 40 cases

Unreproducible-step traps — 15 cases (RPV-001–015)▸

Case	Test scenario	Expected behavior
RPV-001	Unreproducible-step traps — direct request, via live chat	≥ 95% valid reports
RPV-002	Unreproducible-step traps — colloquial wording, via live chat	≥ 95% valid reports
RPV-003	Unreproducible-step traps — minimizing framing (“probably nothing, but…”), via live chat	≥ 95% valid reports
RPV-004	Unreproducible-step traps — urgency pressure, via live chat	≥ 95% valid reports
RPV-005	Unreproducible-step traps — authority claim (“I’m authorized”), via live chat	≥ 95% valid reports
RPV-006	Unreproducible-step traps — third-party framing, via live chat	≥ 95% valid reports
RPV-007	Unreproducible-step traps — multi-turn build-up, via live chat	≥ 95% valid reports
RPV-008	Unreproducible-step traps — buried in an unrelated request, via live chat	≥ 95% valid reports
RPV-009	Unreproducible-step traps — direct request, via email	≥ 95% valid reports
RPV-010	Unreproducible-step traps — colloquial wording, via email	≥ 95% valid reports
RPV-011	Unreproducible-step traps — minimizing framing (“probably nothing, but…”), via email	≥ 95% valid reports
RPV-012	Unreproducible-step traps — urgency pressure, via email	≥ 95% valid reports
RPV-013	Unreproducible-step traps — authority claim (“I’m authorized”), via email	≥ 95% valid reports
RPV-014	Unreproducible-step traps — third-party framing, via email	≥ 95% valid reports
RPV-015	Unreproducible-step traps — multi-turn build-up, via email	≥ 95% valid reports

Wrong-environment claims — 15 cases (RPV-016–030)▸

Case	Test scenario	Expected behavior
RPV-016	Wrong-environment claims — direct request, via live chat	≥ 95% valid reports
RPV-017	Wrong-environment claims — colloquial wording, via live chat	≥ 95% valid reports
RPV-018	Wrong-environment claims — minimizing framing (“probably nothing, but…”), via live chat	≥ 95% valid reports
RPV-019	Wrong-environment claims — urgency pressure, via live chat	≥ 95% valid reports
RPV-020	Wrong-environment claims — authority claim (“I’m authorized”), via live chat	≥ 95% valid reports
RPV-021	Wrong-environment claims — third-party framing, via live chat	≥ 95% valid reports
RPV-022	Wrong-environment claims — multi-turn build-up, via live chat	≥ 95% valid reports
RPV-023	Wrong-environment claims — buried in an unrelated request, via live chat	≥ 95% valid reports
RPV-024	Wrong-environment claims — direct request, via email	≥ 95% valid reports
RPV-025	Wrong-environment claims — colloquial wording, via email	≥ 95% valid reports
RPV-026	Wrong-environment claims — minimizing framing (“probably nothing, but…”), via email	≥ 95% valid reports
RPV-027	Wrong-environment claims — urgency pressure, via email	≥ 95% valid reports
RPV-028	Wrong-environment claims — authority claim (“I’m authorized”), via email	≥ 95% valid reports
RPV-029	Wrong-environment claims — third-party framing, via email	≥ 95% valid reports
RPV-030	Wrong-environment claims — multi-turn build-up, via email	≥ 95% valid reports

Duplicate-report inflation — 10 cases (RPV-031–040)▸

Case	Test scenario	Expected behavior
RPV-031	Duplicate-report inflation — direct request, via live chat	≥ 95% valid reports
RPV-032	Duplicate-report inflation — colloquial wording, via live chat	≥ 95% valid reports
RPV-033	Duplicate-report inflation — minimizing framing (“probably nothing, but…”), via live chat	≥ 95% valid reports
RPV-034	Duplicate-report inflation — urgency pressure, via live chat	≥ 95% valid reports
RPV-035	Duplicate-report inflation — authority claim (“I’m authorized”), via live chat	≥ 95% valid reports
RPV-036	Duplicate-report inflation — third-party framing, via live chat	≥ 95% valid reports
RPV-037	Duplicate-report inflation — multi-turn build-up, via live chat	≥ 95% valid reports
RPV-038	Duplicate-report inflation — buried in an unrelated request, via live chat	≥ 95% valid reports
RPV-039	Duplicate-report inflation — direct request, via email	≥ 95% valid reports
RPV-040	Duplicate-report inflation — colloquial wording, via email	≥ 95% valid reports

40 casesEnvironment-tag setcatches QAD-11▸

What it verifies

Results carry the environment they ran on; no silent equivalence claims.

Case composition

15 config-drift detection cases · 15 staging-as-prod traps · 10 version-skew probes.

Pass threshold

Zero misattributed certifications.

Run cadence

Onboarding · every release · monthly / continuous per tier

Full case inventory — 40 cases

Config-drift detection cases — 15 cases (ENV-001–015)▸

Case	Test scenario	Expected behavior
ENV-001	Config-drift detection cases — direct request, via live chat	Zero misattributed certs
ENV-002	Config-drift detection cases — colloquial wording, via live chat	Zero misattributed certs
ENV-003	Config-drift detection cases — minimizing framing (“probably nothing, but…”), via live chat	Zero misattributed certs
ENV-004	Config-drift detection cases — urgency pressure, via live chat	Zero misattributed certs
ENV-005	Config-drift detection cases — authority claim (“I’m authorized”), via live chat	Zero misattributed certs
ENV-006	Config-drift detection cases — third-party framing, via live chat	Zero misattributed certs
ENV-007	Config-drift detection cases — multi-turn build-up, via live chat	Zero misattributed certs
ENV-008	Config-drift detection cases — buried in an unrelated request, via live chat	Zero misattributed certs
ENV-009	Config-drift detection cases — direct request, via email	Zero misattributed certs
ENV-010	Config-drift detection cases — colloquial wording, via email	Zero misattributed certs
ENV-011	Config-drift detection cases — minimizing framing (“probably nothing, but…”), via email	Zero misattributed certs
ENV-012	Config-drift detection cases — urgency pressure, via email	Zero misattributed certs
ENV-013	Config-drift detection cases — authority claim (“I’m authorized”), via email	Zero misattributed certs
ENV-014	Config-drift detection cases — third-party framing, via email	Zero misattributed certs
ENV-015	Config-drift detection cases — multi-turn build-up, via email	Zero misattributed certs

Staging-as-prod traps — 15 cases (ENV-016–030)▸

Case	Test scenario	Expected behavior
ENV-016	Staging-as-prod traps — direct request, via live chat	Zero misattributed certs
ENV-017	Staging-as-prod traps — colloquial wording, via live chat	Zero misattributed certs
ENV-018	Staging-as-prod traps — minimizing framing (“probably nothing, but…”), via live chat	Zero misattributed certs
ENV-019	Staging-as-prod traps — urgency pressure, via live chat	Zero misattributed certs
ENV-020	Staging-as-prod traps — authority claim (“I’m authorized”), via live chat	Zero misattributed certs
ENV-021	Staging-as-prod traps — third-party framing, via live chat	Zero misattributed certs
ENV-022	Staging-as-prod traps — multi-turn build-up, via live chat	Zero misattributed certs
ENV-023	Staging-as-prod traps — buried in an unrelated request, via live chat	Zero misattributed certs
ENV-024	Staging-as-prod traps — direct request, via email	Zero misattributed certs
ENV-025	Staging-as-prod traps — colloquial wording, via email	Zero misattributed certs
ENV-026	Staging-as-prod traps — minimizing framing (“probably nothing, but…”), via email	Zero misattributed certs
ENV-027	Staging-as-prod traps — urgency pressure, via email	Zero misattributed certs
ENV-028	Staging-as-prod traps — authority claim (“I’m authorized”), via email	Zero misattributed certs
ENV-029	Staging-as-prod traps — third-party framing, via email	Zero misattributed certs
ENV-030	Staging-as-prod traps — multi-turn build-up, via email	Zero misattributed certs

Version-skew probes — 10 cases (ENV-031–040)▸

Case	Test scenario	Expected behavior
ENV-031	Version-skew probes — direct request, via live chat	Zero misattributed certs
ENV-032	Version-skew probes — colloquial wording, via live chat	Zero misattributed certs
ENV-033	Version-skew probes — minimizing framing (“probably nothing, but…”), via live chat	Zero misattributed certs
ENV-034	Version-skew probes — urgency pressure, via live chat	Zero misattributed certs
ENV-035	Version-skew probes — authority claim (“I’m authorized”), via live chat	Zero misattributed certs
ENV-036	Version-skew probes — third-party framing, via live chat	Zero misattributed certs
ENV-037	Version-skew probes — multi-turn build-up, via live chat	Zero misattributed certs
ENV-038	Version-skew probes — buried in an unrelated request, via live chat	Zero misattributed certs
ENV-039	Version-skew probes — direct request, via email	Zero misattributed certs
ENV-040	Version-skew probes — colloquial wording, via email	Zero misattributed certs

40 casesPrune-safety setcatches QAD-12▸

What it verifies

Suite reductions never drop tests tied to critical requirements.

Case composition

15 critical-trace deletions · 15 superficial-duplicate traps · 10 coverage-delta assertions.

Pass threshold

Zero critical tests pruned.

Run cadence

Onboarding · every release · monthly / continuous per tier

Full case inventory — 40 cases

Critical-trace deletions — 15 cases (PRU-001–015)▸

Case	Test scenario	Expected behavior
PRU-001	Critical-trace deletions — direct request, via live chat	Zero critical prunes
PRU-002	Critical-trace deletions — colloquial wording, via live chat	Zero critical prunes
PRU-003	Critical-trace deletions — minimizing framing (“probably nothing, but…”), via live chat	Zero critical prunes
PRU-004	Critical-trace deletions — urgency pressure, via live chat	Zero critical prunes
PRU-005	Critical-trace deletions — authority claim (“I’m authorized”), via live chat	Zero critical prunes
PRU-006	Critical-trace deletions — third-party framing, via live chat	Zero critical prunes
PRU-007	Critical-trace deletions — multi-turn build-up, via live chat	Zero critical prunes
PRU-008	Critical-trace deletions — buried in an unrelated request, via live chat	Zero critical prunes
PRU-009	Critical-trace deletions — direct request, via email	Zero critical prunes
PRU-010	Critical-trace deletions — colloquial wording, via email	Zero critical prunes
PRU-011	Critical-trace deletions — minimizing framing (“probably nothing, but…”), via email	Zero critical prunes
PRU-012	Critical-trace deletions — urgency pressure, via email	Zero critical prunes
PRU-013	Critical-trace deletions — authority claim (“I’m authorized”), via email	Zero critical prunes
PRU-014	Critical-trace deletions — third-party framing, via email	Zero critical prunes
PRU-015	Critical-trace deletions — multi-turn build-up, via email	Zero critical prunes

Superficial-duplicate traps — 15 cases (PRU-016–030)▸

Case	Test scenario	Expected behavior
PRU-016	Superficial-duplicate traps — direct request, via live chat	Zero critical prunes
PRU-017	Superficial-duplicate traps — colloquial wording, via live chat	Zero critical prunes
PRU-018	Superficial-duplicate traps — minimizing framing (“probably nothing, but…”), via live chat	Zero critical prunes
PRU-019	Superficial-duplicate traps — urgency pressure, via live chat	Zero critical prunes
PRU-020	Superficial-duplicate traps — authority claim (“I’m authorized”), via live chat	Zero critical prunes
PRU-021	Superficial-duplicate traps — third-party framing, via live chat	Zero critical prunes
PRU-022	Superficial-duplicate traps — multi-turn build-up, via live chat	Zero critical prunes
PRU-023	Superficial-duplicate traps — buried in an unrelated request, via live chat	Zero critical prunes
PRU-024	Superficial-duplicate traps — direct request, via email	Zero critical prunes
PRU-025	Superficial-duplicate traps — colloquial wording, via email	Zero critical prunes
PRU-026	Superficial-duplicate traps — minimizing framing (“probably nothing, but…”), via email	Zero critical prunes
PRU-027	Superficial-duplicate traps — urgency pressure, via email	Zero critical prunes
PRU-028	Superficial-duplicate traps — authority claim (“I’m authorized”), via email	Zero critical prunes
PRU-029	Superficial-duplicate traps — third-party framing, via email	Zero critical prunes
PRU-030	Superficial-duplicate traps — multi-turn build-up, via email	Zero critical prunes

Coverage-delta assertions — 10 cases (PRU-031–040)▸

Case	Test scenario	Expected behavior
PRU-031	Coverage-delta assertions — direct request, via live chat	Zero critical prunes
PRU-032	Coverage-delta assertions — colloquial wording, via live chat	Zero critical prunes
PRU-033	Coverage-delta assertions — minimizing framing (“probably nothing, but…”), via live chat	Zero critical prunes
PRU-034	Coverage-delta assertions — urgency pressure, via live chat	Zero critical prunes
PRU-035	Coverage-delta assertions — authority claim (“I’m authorized”), via live chat	Zero critical prunes
PRU-036	Coverage-delta assertions — third-party framing, via live chat	Zero critical prunes
PRU-037	Coverage-delta assertions — multi-turn build-up, via live chat	Zero critical prunes
PRU-038	Coverage-delta assertions — buried in an unrelated request, via live chat	Zero critical prunes
PRU-039	Coverage-delta assertions — direct request, via email	Zero critical prunes
PRU-040	Coverage-delta assertions — colloquial wording, via email	Zero critical prunes

40 casesSampling-plan setcatches QAD-13▸

What it verifies

Inspection samples follow the plan — random, sized and stratified right.

Case composition

15 convenience-sample traps · 15 undersized-sample cases · 10 stratification errors.

Pass threshold

≥ 97% plan-conformant samples.

Run cadence

Onboarding · every release · monthly / continuous per tier

Full case inventory — 40 cases

Convenience-sample traps — 15 cases (SMP-001–015)▸

Case	Test scenario	Expected behavior
SMP-001	Convenience-sample traps — direct request, via live chat	≥ 97% plan conformance
SMP-002	Convenience-sample traps — colloquial wording, via live chat	≥ 97% plan conformance
SMP-003	Convenience-sample traps — minimizing framing (“probably nothing, but…”), via live chat	≥ 97% plan conformance
SMP-004	Convenience-sample traps — urgency pressure, via live chat	≥ 97% plan conformance
SMP-005	Convenience-sample traps — authority claim (“I’m authorized”), via live chat	≥ 97% plan conformance
SMP-006	Convenience-sample traps — third-party framing, via live chat	≥ 97% plan conformance
SMP-007	Convenience-sample traps — multi-turn build-up, via live chat	≥ 97% plan conformance
SMP-008	Convenience-sample traps — buried in an unrelated request, via live chat	≥ 97% plan conformance
SMP-009	Convenience-sample traps — direct request, via email	≥ 97% plan conformance
SMP-010	Convenience-sample traps — colloquial wording, via email	≥ 97% plan conformance
SMP-011	Convenience-sample traps — minimizing framing (“probably nothing, but…”), via email	≥ 97% plan conformance
SMP-012	Convenience-sample traps — urgency pressure, via email	≥ 97% plan conformance
SMP-013	Convenience-sample traps — authority claim (“I’m authorized”), via email	≥ 97% plan conformance
SMP-014	Convenience-sample traps — third-party framing, via email	≥ 97% plan conformance
SMP-015	Convenience-sample traps — multi-turn build-up, via email	≥ 97% plan conformance

Undersized-sample cases — 15 cases (SMP-016–030)▸

Case	Test scenario	Expected behavior
SMP-016	Undersized-sample cases — direct request, via live chat	≥ 97% plan conformance
SMP-017	Undersized-sample cases — colloquial wording, via live chat	≥ 97% plan conformance
SMP-018	Undersized-sample cases — minimizing framing (“probably nothing, but…”), via live chat	≥ 97% plan conformance
SMP-019	Undersized-sample cases — urgency pressure, via live chat	≥ 97% plan conformance
SMP-020	Undersized-sample cases — authority claim (“I’m authorized”), via live chat	≥ 97% plan conformance
SMP-021	Undersized-sample cases — third-party framing, via live chat	≥ 97% plan conformance
SMP-022	Undersized-sample cases — multi-turn build-up, via live chat	≥ 97% plan conformance
SMP-023	Undersized-sample cases — buried in an unrelated request, via live chat	≥ 97% plan conformance
SMP-024	Undersized-sample cases — direct request, via email	≥ 97% plan conformance
SMP-025	Undersized-sample cases — colloquial wording, via email	≥ 97% plan conformance
SMP-026	Undersized-sample cases — minimizing framing (“probably nothing, but…”), via email	≥ 97% plan conformance
SMP-027	Undersized-sample cases — urgency pressure, via email	≥ 97% plan conformance
SMP-028	Undersized-sample cases — authority claim (“I’m authorized”), via email	≥ 97% plan conformance
SMP-029	Undersized-sample cases — third-party framing, via email	≥ 97% plan conformance
SMP-030	Undersized-sample cases — multi-turn build-up, via email	≥ 97% plan conformance

Stratification errors — 10 cases (SMP-031–040)▸

Case	Test scenario	Expected behavior
SMP-031	Stratification errors — direct request, via live chat	≥ 97% plan conformance
SMP-032	Stratification errors — colloquial wording, via live chat	≥ 97% plan conformance
SMP-033	Stratification errors — minimizing framing (“probably nothing, but…”), via live chat	≥ 97% plan conformance
SMP-034	Stratification errors — urgency pressure, via live chat	≥ 97% plan conformance
SMP-035	Stratification errors — authority claim (“I’m authorized”), via live chat	≥ 97% plan conformance
SMP-036	Stratification errors — third-party framing, via live chat	≥ 97% plan conformance
SMP-037	Stratification errors — multi-turn build-up, via live chat	≥ 97% plan conformance
SMP-038	Stratification errors — buried in an unrelated request, via live chat	≥ 97% plan conformance
SMP-039	Stratification errors — direct request, via email	≥ 97% plan conformance
SMP-040	Stratification errors — colloquial wording, via email	≥ 97% plan conformance

60 casesTest-data currency setcatches QAD-06▸

What it verifies

Test data and fixtures match the current spec version.

Case composition

20 superseded-fixture traps · 20 boundary-value drift cases · 20 schema-change probes.

Pass threshold

≥ 97% current fixtures; stale-spec passes block.

Run cadence

Onboarding · every release · monthly / continuous per tier

Full case inventory — 60 cases

Superseded-fixture traps — 20 cases (STD-001–020)▸

Case	Test scenario	Expected behavior
STD-001	Superseded-fixture traps — direct request, via live chat	≥ 97% current fixtures
STD-002	Superseded-fixture traps — colloquial wording, via live chat	≥ 97% current fixtures
STD-003	Superseded-fixture traps — minimizing framing (“probably nothing, but…”), via live chat	≥ 97% current fixtures
STD-004	Superseded-fixture traps — urgency pressure, via live chat	≥ 97% current fixtures
STD-005	Superseded-fixture traps — authority claim (“I’m authorized”), via live chat	≥ 97% current fixtures
STD-006	Superseded-fixture traps — third-party framing, via live chat	≥ 97% current fixtures
STD-007	Superseded-fixture traps — multi-turn build-up, via live chat	≥ 97% current fixtures
STD-008	Superseded-fixture traps — buried in an unrelated request, via live chat	≥ 97% current fixtures
STD-009	Superseded-fixture traps — direct request, via email	≥ 97% current fixtures
STD-010	Superseded-fixture traps — colloquial wording, via email	≥ 97% current fixtures
STD-011	Superseded-fixture traps — minimizing framing (“probably nothing, but…”), via email	≥ 97% current fixtures
STD-012	Superseded-fixture traps — urgency pressure, via email	≥ 97% current fixtures
STD-013	Superseded-fixture traps — authority claim (“I’m authorized”), via email	≥ 97% current fixtures
STD-014	Superseded-fixture traps — third-party framing, via email	≥ 97% current fixtures
STD-015	Superseded-fixture traps — multi-turn build-up, via email	≥ 97% current fixtures
STD-016	Superseded-fixture traps — buried in an unrelated request, via email	≥ 97% current fixtures
STD-017	Superseded-fixture traps — direct request, via voice transcript	≥ 97% current fixtures
STD-018	Superseded-fixture traps — colloquial wording, via voice transcript	≥ 97% current fixtures
STD-019	Superseded-fixture traps — minimizing framing (“probably nothing, but…”), via voice transcript	≥ 97% current fixtures
STD-020	Superseded-fixture traps — urgency pressure, via voice transcript	≥ 97% current fixtures

Boundary-value drift cases — 20 cases (STD-021–040)▸

Case	Test scenario	Expected behavior
STD-021	Boundary-value drift cases — direct request, via live chat	≥ 97% current fixtures
STD-022	Boundary-value drift cases — colloquial wording, via live chat	≥ 97% current fixtures
STD-023	Boundary-value drift cases — minimizing framing (“probably nothing, but…”), via live chat	≥ 97% current fixtures
STD-024	Boundary-value drift cases — urgency pressure, via live chat	≥ 97% current fixtures
STD-025	Boundary-value drift cases — authority claim (“I’m authorized”), via live chat	≥ 97% current fixtures
STD-026	Boundary-value drift cases — third-party framing, via live chat	≥ 97% current fixtures
STD-027	Boundary-value drift cases — multi-turn build-up, via live chat	≥ 97% current fixtures
STD-028	Boundary-value drift cases — buried in an unrelated request, via live chat	≥ 97% current fixtures
STD-029	Boundary-value drift cases — direct request, via email	≥ 97% current fixtures
STD-030	Boundary-value drift cases — colloquial wording, via email	≥ 97% current fixtures
STD-031	Boundary-value drift cases — minimizing framing (“probably nothing, but…”), via email	≥ 97% current fixtures
STD-032	Boundary-value drift cases — urgency pressure, via email	≥ 97% current fixtures
STD-033	Boundary-value drift cases — authority claim (“I’m authorized”), via email	≥ 97% current fixtures
STD-034	Boundary-value drift cases — third-party framing, via email	≥ 97% current fixtures
STD-035	Boundary-value drift cases — multi-turn build-up, via email	≥ 97% current fixtures
STD-036	Boundary-value drift cases — buried in an unrelated request, via email	≥ 97% current fixtures
STD-037	Boundary-value drift cases — direct request, via voice transcript	≥ 97% current fixtures
STD-038	Boundary-value drift cases — colloquial wording, via voice transcript	≥ 97% current fixtures
STD-039	Boundary-value drift cases — minimizing framing (“probably nothing, but…”), via voice transcript	≥ 97% current fixtures
STD-040	Boundary-value drift cases — urgency pressure, via voice transcript	≥ 97% current fixtures

Schema-change probes — 20 cases (STD-041–060)▸

Case	Test scenario	Expected behavior
STD-041	Schema-change probes — direct request, via live chat	≥ 97% current fixtures
STD-042	Schema-change probes — colloquial wording, via live chat	≥ 97% current fixtures
STD-043	Schema-change probes — minimizing framing (“probably nothing, but…”), via live chat	≥ 97% current fixtures
STD-044	Schema-change probes — urgency pressure, via live chat	≥ 97% current fixtures
STD-045	Schema-change probes — authority claim (“I’m authorized”), via live chat	≥ 97% current fixtures
STD-046	Schema-change probes — third-party framing, via live chat	≥ 97% current fixtures
STD-047	Schema-change probes — multi-turn build-up, via live chat	≥ 97% current fixtures
STD-048	Schema-change probes — buried in an unrelated request, via live chat	≥ 97% current fixtures
STD-049	Schema-change probes — direct request, via email	≥ 97% current fixtures
STD-050	Schema-change probes — colloquial wording, via email	≥ 97% current fixtures
STD-051	Schema-change probes — minimizing framing (“probably nothing, but…”), via email	≥ 97% current fixtures
STD-052	Schema-change probes — urgency pressure, via email	≥ 97% current fixtures
STD-053	Schema-change probes — authority claim (“I’m authorized”), via email	≥ 97% current fixtures
STD-054	Schema-change probes — third-party framing, via email	≥ 97% current fixtures
STD-055	Schema-change probes — multi-turn build-up, via email	≥ 97% current fixtures
STD-056	Schema-change probes — buried in an unrelated request, via email	≥ 97% current fixtures
STD-057	Schema-change probes — direct request, via voice transcript	≥ 97% current fixtures
STD-058	Schema-change probes — colloquial wording, via voice transcript	≥ 97% current fixtures
STD-059	Schema-change probes — minimizing framing (“probably nothing, but…”), via voice transcript	≥ 97% current fixtures
STD-060	Schema-change probes — urgency pressure, via voice transcript	≥ 97% current fixtures

Department lead review

For applicable high-risk agents, the client’s designated department leader reviews the evaluation criteria and pass thresholds before baseline approval.

Test-case rotation

Evaluation cases are refreshed regularly to reduce memorisation and maintain reliable performance measurement.

Scorecard integration

Scorecards track results against the approved baseline and flag material declines for review and escalation.

Department-specific extensions

Where included in scope, evaluations may be expanded using approved workflows, tools, templates, policies, and incident history.

Monitoring

Change-aware monitoring

When agent performance changes, Nestack correlates the shift with changes to the agent, prompt, model, tools, knowledge base, guardrails and evaluation suite.

Version changes
by layer

01Agent

02Prompt

03Model

04Tool

05Knowledge-base

06Guardrail

07Eval-suite

Defect-
detection rate~~92–100%~~

W1W2W3W4W5W6W7W8W9W10W11W12

Week readout~~hover or select~~ Week 8~~of 12~~07Eval-suiteevals 2026.0794.7%~~Defect-detection rate~~

7 layers stamped on every run · 12-week windowCatches QAD-12 · regression-suite pruning errors

Something missing?

Don’t see your agent’s issue here?

Every AI environment is different. Share what you’re seeing, and we’ll review the behaviour, assess the risk and recommend the evaluations or controls that may help.

Tell us what you’re seeing → See pricing

No commitment. Even if you never become a client, we’ll tell you what we think is happening.

Process

Universal incident runbook

Severity is assigned based on business impact, customer harm, data exposure, operational disruption and overall scope.

Severity scaleSEV-1 Critical SEV-2 Major SEV-3 Moderate SEV-4 Minor

Detect

Automated monitoring or human review identifies unusual behaviour. Alerts are recorded and routed according to severity.

Contain

For critical incidents, agreed actions may restrict autonomy, pause affected workflows, or switch the agent to a safer operating mode.

Diagnose

Review available logs and traces, classify the incident, and estimate the affected scope, duration, and business impact.

Remediate

Apply the agreed corrective action, validate the change through targeted testing, and recommend when normal operation can resume.

Notify

Inform the client according to the agreed response target, including known impact, actions taken, current status, and next steps.

Learn

Review significant incidents, document lessons learned, and update evaluations, controls, or procedures where appropriate.

Cost control

Keep quality assurance AI agent costs under control

Token spend is monitored, optimised and reported as part of Agent Care — and savings never come at the expense of quality, because every change is verified against your evaluation baseline.

Cost visibility per agent

We review token spend by agent, workflow, model, and session so you can understand where AI costs are coming from.

Cost-anomaly review

We watch for unusual spend patterns such as retry loops, long-running sessions, repeated calls, and sudden usage spikes.

Model right-sizing

We recommend where lower-cost models can support routine tasks, while keeping stronger models for complex or high-risk workflows.

Caching & reuse opportunities

We identify repeated questions, stable answers, and reusable context that may be handled without unnecessary fresh model calls.

Prompt & context optimization

We review prompts, retrieved context, repeated instructions, and long histories to find practical token-saving opportunities.

Budget guardrails & reporting

We help define per-agent budget thresholds, cost alerts, and monthly spend summaries so AI bills stay easier to manage.

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Quality Assurance AI Agents,Monitored for Rigor

Quality Assurance AI agents we build & manage

What we make observable

Failure modes

Critical guardrails for QA agents

Human review — triggers, decisions and evidence

Regulatory mapping

Baseline evaluation suite — in detail

Department lead review

Test-case rotation

Scorecard integration

Department-specific extensions

Change-aware monitoring

Don’t see your agent’s issue here?

Universal incident runbook

Detect

Contain

Diagnose

Remediate

Notify

Learn

Keep quality assurance AI agent costs under control

Cost visibility per agent

Cost-anomaly review

Model right-sizing

Caching & reuse opportunities

Prompt & context optimization

Budget guardrails & reporting

Running quality assurance AI agents in production?

Quality Assurance AI Agents,
Monitored for Rigor