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Advertising AI agent · Retail-media network ops

Retail-Media Network Operations AI Agent (Retailer-Side)

Run on-site placements, onboard advertiser catalogues and hold the disclosure every placement requires — this is the retailer's own network, not the brand buying into it, and the network lead owns the policy.

4–6 weeksTypical delivery
Your stackDeployment
Pre-exceptionNetwork lead
Agent CareAfter launch

What this agent does

Runs the auction, not the policy it follows

In
01

An advertiser books a campaign, or a shopper's search sets the on-site auction running.

02

Normalise the catalogue, placement and auction fields, and mark each field's source.

Reason
03

Rank eligible placements against the auction and ranking policy the network lead configured.

04

Apply the disclosure label and format the surface requires — on-site, off-site or in-store.

05

Weigh the result against the retailer's own competing items and prior ranking outcomes.

Decide
06

Flag placements that would out-rank policy, crowd disclosure, or push ad load past its ceiling.

07

Route every policy exception and new advertiser catalogue to the named network lead.

Out
08

Retain the auction inputs, the placement, the disclosure applied and the lead's decision.

09

Execute write actions only inside the approval boundaries agreed during implementation.

Product statement

The agent ranks and discloses the placement; the network lead sets and changes the policy, and the retailer stays accountable for what runs on its own site.

Example workflow

One placement, request to release

AgentHuman
1Placement request receivedAdvertiser campaign booking, catalogue feed, on-site auction call or off-site placement request
2Auction context assembledAdvertiser tier, catalogue data, ranking-policy inputs and prior placements, each sourced
3Ranking proposedRanked placement, disclosure label, ad-load reading and confidence
4Controls appliedPolicy checks, disclosure checks, self-preferencing checks and confidence threshold
No human action required

Stages 1 to 4 run unaided, and nothing serves outside policy at any of them — the agent ranks and discloses, and the network lead's lane opens at the confidence gate.

5DecisionBranches at the confidence threshold
High confidence

Goes to the network lead to approve.

Low confidence

Adds a second monetisation read first.

Network lead review

The placement is held with its auction inputs, its policy flags and the confidence.

Approve · Edit · Escalate to policy review
Approved — cleared to serve
6Ad-server and disclosure systems updatedOnly where write access and approval policy allow it
7Outcome evaluatedRanking accuracy, disclosure outcomes, onboarding results and post-serve corrections
Edits

Every network lead edit is counted in the evaluation.

What should not run autonomously

Human approval stays in control

Outside the boundary — human approval required8 items
Changing an auction or ranking rule.
Setting the on-site ad load.
Approving a creative for the storefront.
Issuing advertiser-facing measurement.
Automation boundaryAgent acts unaided
Run the on-site inventory and placements against.
Onboard advertisers and their catalogues against the configured requirements.
Apply and hold the disclosure label the placement's surface requires.
Produce the measurement report, and hold it for release.
Any write happens inside the boundaries agreed at implementation, never ahead of the lead.
Resolving a billing or rebate dispute.
Declaring the ranking self-preferencing-free.
Certifying a metric as MRC-accredited.
Changes to policy, escalation rules or approval thresholds.

Example output

One placement, annotated

Everything the agent ranks is attached to the auction it was drawn from.

Placement-ranking output · single placementIllustrative example
Placement
Finding
Disclosure status
Evidence source
Confidence
Attribution
Own-brand ranking tie
A private-label item and a paid placement tie in the ranking, one rung above where the auction score alone would place it
Disclosure: label pending
This week's auction log
83%
Network lead name on file
As receivedTaken from this week's auction log and the disclosure config — nothing on this side is written by the agent.
Evidence checked Auction score log Disclosure config Advertiser catalogue
Why it's flaggedThe tie sits above where the auction score alone would place it.
ActionApproveEditEscalate to policy review
What the score decidesBelow the configured threshold the flag picks up a second monetisation read before.

Value

Where AI adds value

The same four claims, placed at the point in the workflow where each one applies.

Where the value landsValue 01 – 04
Every placementFrom the auction log
03Ranking

Rank every placement

Score each candidate against the catalogue data and the network lead's written auction and disclosure policy.

01Approved path

The house marks its own homework

A retailer that sells the ads, sells the private-label item beside them and reports the results sits on every side of the sale — the reason the rules stay a written policy a person set, not a claim of neutrality.

02Human review

Send review to the exceptions

Policy exceptions and low-confidence rankings are marked, so the lead's read starts where the conflict concentrates.

04Build an evidence trail

The placement, the auction rule behind it and the network lead who set it stay on the campaign.

Integrations

Typical integrations

Five system groups connect to the same agent. Which of them are in scope is decided in discovery.

Commerce & catalogueSalesforce Commerce · Shopify Plus
Akeneo PIM · Salsify
Ad serving & auctionCriteo Retail Media · Koddi
Epsilon Retail Media · Kevel
Off-site & in-storeThe Trade Desk · DV360
Cooler Screens · Vibenomics

Agent

Retail-media network operations

Reads the auction
Applies the policy
Holds for the lead

Measurement & clean roomsLiveRamp · Habu
InfoSum · Snowflake Clean Rooms
Observability & evaluationOpenTelemetry · Langfuse
Supported monitoring/evaluation sources

Integration availability depends on the client's existing systems and API access.

Agent controls

Six layers between the model and the auction

Each control encloses the one beneath. What none of them catches is named in the map below.

L6 · Outermost — last line of defenceInward → L1 · closest to the model
L6Rollback / safe modeDrop ranking to reporting-only when evaluation or production signals degrade.Roll back
L5Version monitoringTrack model, prompt, auction-rule and policy changes.Track
L4TraceabilityRecord the auction inputs, the ranking, the flags and the lead's decision.Record
L3Network lead approvalHold exceptions for the named lead; it governs release, not whether the ranking is right.Gate
L2Policy guardrailsTest every placement against configured auction and disclosure rules; a failure returns it.Restrict
L1Confidence thresholdsRoute low-confidence rankings to a second monetisation read.Require review
Model coreRanking produced — placement, disclosure label and confidence
L1 – L2Test whether a placement may stand
L3Puts the release in the network lead's hands
L4 – L5Keep the placement and the rule behind it
L6Drops to reporting only when signals degrade

How Nestack evaluates it

Evaluate the ranking workflow — not only the served placement.

Coverage runs the whole depth of the workflow, and every layer is cut by slice.

Surface — the placement the shopper sees
Depth of coverage ▼
E1Final-output evaluationDid the served placement match the auction result and its disclosure label?
E2Step-level evaluationDid the agent use the current policy, catalogue and advertiser-tier data?
E3Tool evaluationDid it read and write the correct auction, catalogue and placement record?
E4Confidence calibrationDo low-confidence rankings actually attract more lead escalations?
E5Slice evaluationHow does performance change across specific advertiser tiers?
E6Business outcomeHow many placements needed a lead correction after serving?
Floor — the outcome the retailer answers for

Failure modes

Where each failure originates in the agent

Seven failure modes, placed at the stage each one originates.

Agent lifecycleDirection of processing →
01 · Retrieval1 mode
NO-03

Off-site inventory list stale

The off-site placement list read is a cached copy that omits newly added MFA-risk domains.

Stage gathersAuction logs, catalogue feed and policy rules
02 · Reasoning2 modes
NO-04

Clean-room output misread

A retailer-hosted clean-room result is reasoned about as an independent audit.

NO-06

Self-preferencing unflagged

A ranking lift toward the retailer's own item clears without a policy flag.

Stage proposesPlacement, disclosure label and confidence
03 · Tool / write2 modes
NO-02

Low-confidence auto-release

A placement serves before the confidence threshold is met.

NO-05

Duplicate catalogue onboarded

The same advertiser catalogue is scored twice under different IDs.

Stage writesOnly where write access and approval policy allow it
04 · Output1 mode
NO-01

Disclosure label omitted

A ranked placement returns to the surface with no 'Ad' label attached.

Stage returnsThe placement the network lead approves
05 · Change / Version1 mode
NO-07

Silent attribution-window drift

A model or config change widens the attribution window without disclosure to advertisers.

Stage tracksModel, auction policy and it is logged.
Sev-1 · served outside the boundary Sev-2 · a placement serves without disclosure Sev-3 · signal degrades, routes to review

Affected slices

A clean network rate can hide one advertiser tier

A network-level disclosure rate can look complete while the gap concentrates in one advertiser tier. Nestack reports the policy-exception rate by tier, not only in total.

Slice performance — reported separately, not only in aggregateIllustrative example
SliceFailure rateLift Lift vs. thresholdStatus
Placements competing with own-brand items7.6%3.9× Review
In-store and off-site extensions5.8%3.0× Review
Newly onboarded advertiser catalogues3.4%1.8× Watch
On-site sponsored product, steady state1.9%0.7× Normal
Bar: policy-exception rate lift vs. the steady-state on-site baseline · scale 0–4.0× · tick at 2.0× 2 of 4 slices over threshold

Evidence-linked improvement

Every cycle ends with one more disclosure case

A cycle closes when the undisclosed placement is a regression case the next release must pass. That suite is what the next campaign.

Improvement cycle · five stagesSwitchback — the path turns at Improve and returns at Learn
01Detect

Policy-exception rate rises in an advertiser tier.

02Diagnose

The auction the house also competed in is checked against the policy, the disclosure label and the advertiser tier until one cause explains it.

03Improve

The fix is versioned, with the campaigns that motivated it attached.

04Verify

Nothing releases until the affected disclosure cases pass again.

05Learn

The case is kept permanently, and the auction rules change with it.

Learn → DetectThe return edge. Detection next time runs against a suite one case longer.

Typical build scope

Twelve workstreams across six weeks

The build scope read against the delivery timeline. Week structure follows the six-week plan — discovery, sources, ranking workflow, evaluation, integration, then production validation and handover.

Workstream Week 1Week 2Week 3Week 4Week 5Week 6
01RMN discovery and boundary definition and it is logged..
02Commerce and ad-server source assessment.
03Auction and disclosure policy mapping and rule mapping.
04Catalogue ingestion and normalisation.
05Ranking logic and disclosure-label binding.
06Confidence scoring and exception routing.
07Network lead approval workflow.
08Commerce and ad-server integration.
09Disclosure and ranking cases.
10Guardrails and publishing controls.
11Campaign-trail instrumentation.
12Deployment, documentation and Agent Care handover.
12 workstreams · 6 weeks · bar shows the weeks a workstream is active — several run in parallel Final scope and sequence confirmed in discovery

Engagement tiers

What each tier includes

Rows are the capabilities named in each tier's scope. Higher tiers include everything below them.

Capability✓ in scope · — not at this tier PilotOne banner, one team ProductionProduction ad-server access AdvancedMultiple banners / entities
Introduced at Pilot
Ranking against your policy and rules
Network lead approval
Disclosure baseline
Introduced at Production
Reporting by advertiser
Escalation workflow in your systems
Approved catalogue write-back
Commerce-platform integration
Introduced at Advanced
Multi-banner auction rules
Multi-stage lead approvals
High auction volume
Multi-banner network controls
Build price From $5,000 From $8,000 Custom quote
Final build priceConfirmed after discovery based on integrations, workflow complexity, transaction volume, approval controls and deployment requirements.
Separate from buildBuild pricing is separate from recurring Agent Care, which covers managed monitoring, evaluations, incidents and verified improvements after launch.

What we need from you

What you bring, and what we build with it

Each input maps to a piece of build scope and a week in the delivery timeline.

You bringWe build with it
01Your catalogue feeds and current placement inventory Catalogue ingestion and placement-field mappingWeek 1
02Representative flagged and cleared placements Ranking baseline, disclosure binding and policy extractionWeek 2
03Your written auction, ranking and disclosure policy Auction, ranking and disclosure policy mappingWeek 1
04Access to relevant APIs, feeds or exports Commerce-platform and ad-server assessment, then integration setupWeek 2
05Placements you would not want served Ranking cases and failure-mode testingWeek 4
06What no placement may hide Confidence scoring, exception routing, guardrails and approval controlsWeek 3
07Named network leads to review placements Network lead approval workflow, then pilot and production validationWeeks 5–6
Nothing else is required Deployment, documentation and Agent Care handover are ours.

Delivery timeline

Four phases across six weeks

Phases cover the weeks the work truly takes, which is why the fifth carries evaluation and pilot.

Phase W1W2W3W4W5W6
Discovery W1
Build W2 – W3
Evaluate W4 – W5
Pilot & Launch W5 – W6
Week focus W1RMN operations discovery, policy mapping and the automation boundary W2Commerce-platform integration and the ranking baseline W3Ranking workflow, confidence logic and disclosure controls W4Evaluation suite, disclosure checks and failure-mode testing W5Ad-server integration, pilot placements and targeted corrections W6One campaign cycle served under the network lead, then Agent Care handover
Reading the bandA bar covers only the weeks its work is named in — the week 5 overlap is real, not padding.
At the end of W6Validation closes on live campaigns and Agent Care picks up monitoring.
DurationSix-week plan shown · typical delivery 4–6 weeks depending on scope confirmed in discovery.

Next step · Advertising AI agent

Build a retail-media network operations agent around your auction and disclosure policy.

Show us your placement inventory, your written auction policy and who signs off on onboarding. Bring in your network lead — we'll map where policy exceptions actually surface, and name exactly what stays theirs to decide.

Nestack Agents · Retail-media network operationsAGT-AM-19 · Agent Care available after launch