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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.

Typical delivery
4–6 weeks
Deployment
Your stack
Network lead
Pre-exception
After launch
Agent Care

What this agent does

Runs the auction, not the policy it follows

In

  1. 01

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

  2. 02

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

Reason

  1. 03

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

  2. 04

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

  3. 05

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

Decide

  1. 06

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

  2. 07

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

Out

  1. 08

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

  2. 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

  1. 1Placement request receivedAdvertiser campaign booking, catalogue feed, on-site auction call or off-site placement request
  2. 2Auction context assembledAdvertiser tier, catalogue data, ranking-policy inputs and prior placements, each sourced
  3. 3Ranking proposedRanked placement, disclosure label, ad-load reading and confidence
  4. 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

  1. 6Ad-server and disclosure systems updatedOnly where write access and approval policy allow it
  2. 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 required

8 items

  • Changing an auction or ranking rule.
    • Setting the on-site ad load.
    • Approving a creative for the storefront.
    • Issuing advertiser-facing measurement.

  • Automation boundary

    Agent 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 placement

Illustrative example
Placement
Own-brand ranking tie
Finding
A private-label item and a paid placement tie in the ranking, one rung above where the auction score alone would place it
Disclosure status
Disclosure: label pending
Evidence source
This week's auction log
Confidence
83%
Attribution
Network lead name on file

As received

Taken 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 flagged

The tie sits above where the auction score alone would place it.

What the score decides

Below the configured threshold the flag picks up a second monetisation read before.

Action

ApproveEditEscalate to policy review

Value

Where AI adds value

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

Where the value lands

Value 01 – 04

Every placement

From 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.

04

Build 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 & catalogue

    • Salesforce Commerce
    • Shopify Plus
    • Akeneo PIM
    • Salsify

  • Ad serving & auction

    • Criteo Retail Media
    • Koddi
    • Epsilon Retail Media
    • Kevel

  • Off-site & in-store

    • The Trade Desk
    • DV360
    • Cooler Screens
    • Vibenomics

Agent

Retail-media network operations

  • Reads the auction
  • Applies the policy
  • Holds for the lead

  • Measurement & clean rooms

    • LiveRamp
    • Habu
    • InfoSum
    • Snowflake Clean Rooms

  • Observability & evaluation

    • OpenTelemetry
    • 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 defence

Inward → 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 – L2
Test whether a placement may stand
L3
Puts the release in the network lead's hands
L4 – L5
Keep the placement and the rule behind it
L6
Drops 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

  1. E1Final-output evaluationDid the served placement match the auction result and its disclosure label?
  2. E2Step-level evaluationDid the agent use the current policy, catalogue and advertiser-tier data?
  3. E3Tool evaluationDid it read and write the correct auction, catalogue and placement record?
  4. E4Confidence calibrationDo low-confidence rankings actually attract more lead escalations?
  5. E5Slice evaluationHow does performance change across specific advertiser tiers?
  6. 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 lifecycle

Direction of processing →

  1. 01 · Retrieval

    1 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 gathers

    Auction logs, catalogue feed and policy rules

  2. 02 · Reasoning

    2 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 proposes

    Placement, disclosure label and confidence

  3. 03 · Tool / write

    2 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 writes

    Only where write access and approval policy allow it

  4. 04 · Output

    1 mode

    • NO-01

      Disclosure label omitted

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

    Stage returns

    The placement the network lead approves

  5. 05 · Change / Version

    1 mode

    • NO-07

      Silent attribution-window drift

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

    Stage tracks

    Model, 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
See the complete Advertising failure-mode catalogue

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 aggregate

Illustrative example
SliceFailure rateLiftLift 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 stages

Switchback — the path turns at Improve and returns at Learn

  1. 01

    Detect

    Policy-exception rate rises in an advertiser tier.

  2. 02

    Diagnose

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

  3. 03

    Improve

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

  4. 04

    Verify

    Nothing releases until the affected disclosure cases pass again.

  5. 05

    Learn

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

  6. Learn → Detect

    The 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

  1. 01RMN discovery and boundary definition and it is logged..Week 1
  2. 02Commerce and ad-server source assessment.Week 1, Week 2
  3. 03Auction and disclosure policy mapping and rule mapping.Week 1, Week 2
  4. 04Catalogue ingestion and normalisation.Week 2
  5. 05Ranking logic and disclosure-label binding.Week 2, Week 3
  6. 06Confidence scoring and exception routing.Week 3
  7. 07Network lead approval workflow.Week 3, Week 4
  8. 08Commerce and ad-server integration.Week 5
  9. 09Disclosure and ranking cases.Week 4, Week 5
  10. 10Guardrails and publishing controls.Week 4
  11. 11Campaign-trail instrumentation.Week 5, Week 6
  12. 12Deployment, documentation and Agent Care handover.Week 6

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 tierPilotOne banner, one teamProductionProduction ad-server accessAdvancedMultiple 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 priceFrom $5,000From $8,000Custom quote
Final build price
Confirmed after discovery based on integrations, workflow complexity, transaction volume, approval controls and deployment requirements.
Separate from build
Build 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.

  1. 01Your catalogue feeds and current placement inventory

    Catalogue ingestion and placement-field mapping

    Week 1

  2. 02Representative flagged and cleared placements

    Ranking baseline, disclosure binding and policy extraction

    Week 2

  3. 03Your written auction, ranking and disclosure policy

    Auction, ranking and disclosure policy mapping

    Week 1

  4. 04Access to relevant APIs, feeds or exports

    Commerce-platform and ad-server assessment, then integration setup

    Week 2

  5. 05Placements you would not want served

    Ranking cases and failure-mode testing

    Week 4

  6. 06What no placement may hide

    Confidence scoring, exception routing, guardrails and approval controls

    Week 3

  7. 07Named network leads to review placements

    Network lead approval workflow, then pilot and production validation

    Weeks 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

W1

W2

W3

W4

W5

W6

  1. DiscoveryW1
  2. BuildW2 – W3
  3. EvaluateW4 – W5
  4. Pilot & LaunchW5 – 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 band
A bar covers only the weeks its work is named in — the week 5 overlap is real, not padding.
At the end of W6
Validation closes on live campaigns and Agent Care picks up monitoring.
Duration
Six-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.

Typical delivery
4–6 weeks
Deployment
Your commerce platform
Network lead
Pre-exception
After launch
Agent Care available

Nestack Agents · Retail-media network operations

AGT-AM-19 · Agent Care available after launch

Build a retail-media network agent

Four questions so the call starts from your actual setup — then pick a time that suits you.

About you
Your network

No obligation. We’ll map the workflow, name the automation boundary and tell you if an agent is the wrong answer.