Campaign Governance specification

Experimental. Campaign governance is part of AdCP 3.0 as an experimental surface — it may change between 3.x releases with at least 6 weeks’ notice. Sellers implementing it MUST declare governance.campaign in experimental_features. See experimental status for the full contract.

Status: Request for Comments Last Updated: March 2026 The key words “MUST”, “MUST NOT”, “REQUIRED”, “SHALL”, “SHALL NOT”, “SHOULD”, “SHOULD NOT”, “RECOMMENDED”, “MAY”, and “OPTIONAL” in this document are to be interpreted as described in RFC 2119. This document defines the data models, validation logic, and integration patterns for Campaign Governance.

Campaign plan

The campaign plan is the source of truth for all validation. Plans are pushed to the governance agent via sync_plans and define the plan parameters for a campaign — budget limits, channels, flight dates, and plan markets. The governance agent resolves applicable policies from the brand’s compliance configuration. Plans can also reference registry policies directly via policy_ids and include campaign-specific rules via custom_policies.

{
  "plan_id": "plan_q1_2026_launch",
  "brand": {
    "domain": "acmecorp.com"
  },
  "objectives": "Drive awareness for spring product launch among 25-54 adults in the US, focusing on premium video and high-impact display.",
  "budget": {
    "total": 500000,
    "currency": "USD",
    "reallocation_threshold": 25000,
    "per_seller_max_pct": 40
  },
  "channels": {
    "required": ["olv"],
    "allowed": ["olv", "display", "ctv", "audio"],
    "mix_targets": {
      "olv": { "min_pct": 40, "max_pct": 70 },
      "display": { "min_pct": 10, "max_pct": 30 },
      "ctv": { "min_pct": 0, "max_pct": 20 },
      "audio": { "min_pct": 0, "max_pct": 10 }
    }
  },
  "flight": {
    "start": "2026-03-15T00:00:00Z",
    "end": "2026-06-15T00:00:00Z"
  },
  "countries": ["US"],
  "policy_ids": ["us_coppa", "alcohol_advertising"],
  "custom_policies": [
    {
      "policy_id": "no_competitor_adjacency",
      "enforcement": "must",
      "policy": "No advertising adjacent to competitor brand content."
    }
  ],
  "approved_sellers": null,
  "ext": {}
}

Purchase types

Governance plans govern all financial commitments, not just media buys. The purchase_type field on check_governance identifies which kind of commitment is being validated:

Purchase type	Tool	What’s governed
`media_buy` (default)	`create_media_buy`, `update_media_buy`	Media inventory purchases
`rights_license`	`acquire_rights`, `update_rights`	Brand rights licensing fees
`signal_activation`	`activate_signal`	Data signal activation fees
`creative_services`	`build_creative`	Creative generation fees

All purchase types share the same governance loop: sync_plans → check_governance → execute → report_plan_outcome. The governance agent validates budget authority, geo compliance, and flight compliance across all types. Media-buy-specific validations (channel compliance, seller concentration, delivery pacing) apply only when purchase_type is media_buy or when the payload contains the relevant fields. When purchase_type is omitted, the governance agent assumes media_buy.

Future purchase types: Content standards, property list curation, and measurement/verification services (brand lift studies, viewability, fraud detection) all carry pricing_options in their schemas and bill through report_usage. These services currently lack an explicit activation tool where the buyer commits to the service — the billing relationship is implicit. When the protocol adds activation surfaces for these services, corresponding purchase types will be added to enable governance checks at the point of commitment.

Budget reallocation

budget.reallocation_threshold (required number) governs budget reallocation autonomy. It does not cover mandatory human review of decisions that affect data subjects — for that, see the plan-level human_review_required field.

Value	Meaning
`0`	Every reallocation requires human approval
positive number below `budget.total`	Agent may reallocate up to this amount without escalation; larger moves escalate for human review
equal to `budget.total` (or greater)	Agent may reallocate freely within the plan’s total budget

Budget allocations

Plans can optionally partition the total budget across purchase types using allocations:

{
  "budget": {
    "total": 500000,
    "currency": "USD",
    "reallocation_threshold": 25000,
    "allocations": {
      "media_buy": { "amount": 400000 },
      "rights_license": { "amount": 75000 },
      "signal_activation": { "amount": 25000 }
    }
  }
}

When allocations is present, the governance agent validates spend against both the per-type allocation and the overall total. When absent, all spend counts against the single total regardless of purchase type. Allocations are guardrails, not hard partitions — the sum of allocations MAY differ from the total. When allocations is present but a purchase type is not listed (e.g., signal_activation is attempted against a plan that only allocates for media_buy and rights_license), the governance agent validates the action against the plan’s total budget only. Unlisted types are not denied — they draw from the shared pool. To restrict spending to listed types only, set custom_policies with explicit constraints.

Human review required

human_review_required is a plan-level boolean (default false) that mandates human oversight of every action on the plan, independent of budget reallocation autonomy. The governance agent sets human_review_required: true automatically when any resolved policy or policy_category on the plan carries requires_human_review: true. This includes regulated verticals such as fair_housing, fair_lending, fair_employment, and pharmaceutical_advertising, and the eu_ai_act_annex_iii policy covering decisions in Annex III use cases. When human_review_required is true, the governance agent MUST escalate any action on the plan for human review before execution — regardless of the plan’s reallocation_threshold. A permissive reallocation threshold does not bypass human review when the plan carries human_review_required: true; the two dimensions compose. This field is distinct from budget.reallocation_threshold:

Field	Scope	Purpose
`budget.reallocation_threshold`	Operational	Controls agent autonomy for budget reallocation within the plan
`human_review_required`	Regulatory	Mandates human review of decisions affecting individuals (e.g., GDPR Art 22, EU AI Act Annex III)

Callers MAY set human_review_required: true explicitly on a plan even when no triggering policy is present. Callers MUST NOT set it to false to override a policy that requires human review — the governance agent re-evaluates the flag from resolved policies on every sync and overrides a caller-supplied false when a triggering policy is present.

Spend-commit invocation

Buyer-side governance invocation is enforceable, not advisory. When a governance agent is configured on the plan, the buyer agent MUST invoke check_governance before sending any spend-commit request to a seller — full stop — producing an intent-phase governance_context token to attach to that request. The governance agent decides internally whether to auto-approve, apply conditions, deny, or escalate to human review per the plan’s budget.reallocation_threshold and human_review_required fields. No dollar figures, no baseline arithmetic, no operator-declared floors appear in the invocation rule — those auto-approve fast-paths belong inside the governance agent’s own policy, not in the buyer’s decision about whether to call at all.

Spend-commit tasks

The invocation MUST applies to every AdCP task that attaches financial obligation at the moment of the request:

create_media_buy — committed budget across packages
update_media_buy — incremental commit delta (new budget − previously committed)
acquire_rights — rights pricing
update_rights — incremental commit delta
activate_signal — activation fees
build_creative — creative generation fees
Any future spend-commit task

Invocation is NOT required for discovery tasks (e.g., get_products, get_signals), reporting tasks (e.g., get_media_buy_delivery), or operational-status tasks. The MUST fires specifically at the moment of financial obligation. When no governance agent is configured on the plan, check_governance invocation is neither required nor meaningful — there is nothing to call. Sellers MAY refuse to transact on plans lacking a configured governance agent as a matter of their own commercial policy (enterprise sellers typically will); the protocol does not mandate one, and the brand’s published configuration is how a seller discovers whether one exists. Orchestrators fanning out the same plan to multiple sellers produce one intent token per seller, because aud is bound byte-for-byte to the target seller. This is the correct protocol shape, but it means the governance agent sees the buyer’s full shopping list on a plan. Operators who treat shopping intent as commercially sensitive SHOULD choose governance agents whose data-handling posture they trust.

Seller enforcement

A seller receiving a spend-commit request for a plan with a configured governance agent MUST require a valid, in-date intent-phase governance_context token on the request, verified per the seller verification checklist. The token MUST carry phase: "intent", match the request’s plan_id (via sub), and be addressed (aud) to this seller. A request without a token, with a token that fails verification, or with a token issued for a different plan, a different seller, or a non-intent phase MUST be rejected with PERMISSION_DENIED. The seller then performs its own execution check by calling check_governance with planned_delivery and the received governance_context; that call produces the purchase-phase token (bound to the seller-assigned media_buy_id) used for the remainder of the media buy lifecycle. This two-step flow is what makes the buyer-side MUST real: a buyer that skips check_governance cannot produce a valid intent token, and the spend-commit is rejected before the seller ever reaches its execution check. Sellers MUST persist the accepted intent token and any lifecycle tokens they subsequently hold, keyed at minimum by jti with iss, aud, sub (plan_id), phase, decision outcome, and the timestamp of acceptance. Retention follows the seller’s regulatory retention period. Without seller-side retention, the audit log is single-sourced from the governance agent; independent reconciliation between seller records and get_plan_audit_logs is the cross-check that catches a compromised or misbehaving governance agent. Seller-side governance (if the seller itself has configured a governance agent on the account) is an independent layer. A buyer’s successful check_governance does not obligate the seller to accept the request; the seller’s own compliance policies MAY still reject the action via PERMISSION_DENIED. An approved token carries an exp that is authoritative at verification time; a policy change inside the token’s validity window is accepted residual risk of any signed-decision system. Tight exp values (intent tokens SHOULD expire within 15 minutes per the JWS profile) bound the window rather than closing it. Operators who cannot tolerate the window MUST set reallocation_threshold to 0 or human_review_required: true so every action goes through an internal human review regardless of caching.

Audit logging

Every check_governance invocation MUST produce an audit log entry — retrievable via get_plan_audit_logs — capturing:

Invocation timestamp as a timezone-offset ISO 8601 string
Tool being validated (create_media_buy, acquire_rights, etc.) and commit amount in the plan’s currency
Outcome (approved, denied, or conditions; and whether human review was invoked internally)
Human actor identity and authority, when a human signal was recorded
check_id for cross-reference from the downstream spend-commit task’s audit entry and from report_plan_outcome

The buyer-side intent check and the seller-side execution check each produce a distinct check_id; report_plan_outcome correlates via plan_id rather than a single check identifier. Auditors reconstructing a spend-commit reconcile the buyer-side entry, the seller-side entry, and the seller’s persisted token record.

Interaction with idempotency

Retry of an identical payload carrying the prior intent-phase governance_context: no re-invocation. The cached governance response is reused; the token signature and freshness are re-validated. Seller-side replay-dedup keys (see the verification checklist) MUST treat a repeated jti carrying the same idempotency_key as a legitimate retry rather than a replay attack — this is the one narrow carve-out.
Re-plan with a different payload (new idempotency_key per Idempotency): a fresh check_governance invocation is required. A new governance_context token is issued.

A retry after a human approval does not re-invoke governance; the existing token remains the authorization until it expires. Post-execution lifecycle retries operate against the seller’s purchase-phase token, which is a distinct artifact governed by the seller’s execution check, not the buyer’s intent check.

Channel mix targets

The mix_targets field defines acceptable allocation ranges. The governance agent validates that aggregate spend across all media buys stays within these ranges. A create_media_buy that would push video spend above 70% of total budget triggers a conditions or denied status.

Delegations

Plans can include a delegations array that specifies which agents are authorized to execute against the plan and with what constraints. This makes the brand–agency delegation relationship explicit in the protocol.

{
  "plan_id": "plan_q1_2026_launch",
  "brand": { "domain": "acmecorp.com" },
  "delegations": [
    {
      "agent_url": "https://buying.pinnacle-media.com",
      "authority": "full",
      "budget_limit": { "amount": 300000, "currency": "USD" },
      "markets": ["FR", "DE", "GB"],
      "expires_at": "2026-06-30T00:00:00Z"
    },
    {
      "agent_url": "https://buying.nova-agency.com",
      "authority": "execute_only",
      "markets": ["US"],
      "expires_at": "2026-06-30T00:00:00Z"
    }
  ]
}

Authority levels:

Level	Meaning
`full`	Can execute any action within the delegation’s budget and market constraints
`execute_only`	Can execute pre-approved actions but cannot initiate new campaigns or reallocate budget
`propose_only`	Can propose actions for governance review but cannot execute without explicit approval

When delegations are present, the governance agent validates that the caller URL in check_governance matches a delegation’s agent_url before approving actions. Matching is by exact URI comparison (case-sensitive, after normalization per RFC 3986). An agent requesting a media buy in France must have a delegation that includes France in its markets. An agent with execute_only authority cannot reallocate budget between channels. When delegations are absent, the governance agent does not restrict which agents can act on the plan.

delegations.authority governs what a delegated executor-agent can do on behalf of the plan. It is unrelated to the plan’s budget autonomy (budget.reallocation_threshold / budget.reallocation_unlimited) and unrelated to plan.human_review_required. Three separate concerns: per-agent scope, budget ops, and per-decision review.

Portfolio governance

For holding companies and multi-brand organizations, a plan can include a portfolio object that defines cross-brand constraints. Portfolio plans govern member plans — any action validated against a member plan is also validated against the portfolio plan’s constraints.

{
  "plan_id": "portfolio_q1_2026_global",
  "brand": { "domain": "acmecorp.com" },
  "objectives": "Global Q1 media governance across all Acme brands",
  "budget": { "total": 50000000, "currency": "USD", "reallocation_threshold": 2000000 },
  "flight": { "start": "2026-01-01T00:00:00Z", "end": "2026-06-30T00:00:00Z" },
  "countries": ["US", "GB", "FR", "DE", "JP"],
  "portfolio": {
    "member_plan_ids": ["plan_sparkle_q1", "plan_glow_q1", "plan_nova_q1"],
    "total_budget_cap": { "amount": 50000000, "currency": "USD" },
    "shared_policy_ids": ["eu_gdpr_advertising", "eu_ai_act_article_50"],
    "shared_exclusions": [
      {
        "policy_id": "no_competitor_properties",
        "enforcement": "must",
        "policy": "No advertising on properties owned by competitor holding companies."
      }
    ]
  }
}

Portfolio constraints:

total_budget_cap: Maximum aggregate spend across all member plans. The governance agent tracks committed budget across all member plans and denies actions that would exceed the cap.
shared_policy_ids: Registry policies enforced across all member plans, regardless of individual brand compliance configuration. Corporate-level regulations that no brand team can override.
shared_exclusions: Bespoke exclusion policies applied to all member plans, using the PolicyEntry shape. Additive only — same constraint as plan-level custom_policies.

The governance agent validates member plan actions against both the member plan’s own constraints and the portfolio plan’s constraints. A denial from either level blocks the action. When a portfolio plan references a member_plan_id that the governance agent does not yet recognize, the governance agent SHOULD accept the portfolio plan and begin enforcing portfolio constraints as member plans are synced. This allows portfolio plans to be synced before their member plans without requiring a specific ordering.

Concurrency: An orchestrator may send create_media_buy requests to multiple sellers simultaneously, each triggering a committed check. Budget checks are point-in-time and do not reserve budget, so concurrent approvals may together exceed the plan budget. The governance agent detects overspend at outcome reporting time. To prevent concurrent overspend, use delegations with per-agent budget_limit to partition the budget across executing agents.

Aggregated-spend evaluation (fragmentation defense)

Governance thresholds (reallocation_threshold, human_review_required trigger points, registry-policy dollar floors) MUST be evaluated against aggregated committed spend over a trailing window, not per-plan or per-media-buy in isolation. A buyer that fragments a $999,900 intended spend into 100 × $9,999 buys — each individually below an operator’s $10,000 human-review threshold — would otherwise bypass review entirely. This is a fragmentation attack on governance, not a legitimate usage pattern, and the governance agent MUST close it. Governance agents MUST aggregate committed spend across all of the following when evaluating any threshold:

All plans attributable to the same (buyer_agent, seller_agent, account_id) tuple — fragmentation across plans on the same account does not reset the aggregate. Delegated sub-agents (see Delegations) share the delegating buyer’s aggregate: the buyer_agent element of the key is the delegating principal, not the sub-agent’s agent_url. A delegation does NOT mint a fresh per-agent aggregation window, because otherwise the delegation surface itself reopens the fragmentation hole ($999,900 split across 100 sub-agents, each getting their own $9,999 budget).
Every spend-commit task — fragmentation across task surfaces does not reset the aggregate. The spend-commit task inventory is the single authoritative list; new spend-commit tasks added there join the aggregate automatically, without a separate edit in this section.
The trailing window declared via the governance.aggregation_window_days capability (see get_adcp_capabilities below). The window slides with wall-clock time, not plan boundaries.

Evaluation-time semantics (testable). At the moment of a spend-commit, the governance agent computes

aggregate = sum(c.amount for c in commit_history
                where c.key == this.key
                  and c.ts > now - aggregation_window_days × 86400s)
            + this.amount

then evaluates aggregate against each applicable threshold. The current incoming commit is included in the sum. Commits that were denied outright do not contribute; commits approved or approved-with-conditions do. now is the governance agent’s wall-clock time at evaluation; sliding-window boundaries are not snapped to plan or calendar boundaries. Commitments are sticky within the window. c.amount is the amount committed at approval time, not the delivered amount. Under-delivery, cancellation, makegoods, and post-approval budget reductions MUST NOT decrement a commit’s contribution to the aggregate before the trailing window rolls it off. Otherwise a buyer could free fragmentation headroom by cancelling an approved commit and immediately re-committing sub-threshold — full spend moves across the round trip, each leg passes in isolation. An update_media_buy that increases committed budget enters as a delta (new committed budget − prior committed); a decrease does not decrement. When an individual commit would be below a threshold in isolation but pushes the trailing-window aggregate above the threshold, the governance agent MUST apply the threshold’s consequence (human review escalation, denial, or conditions) to that commit. Governance agents MAY expose an aggregate_committed field on get_plan_audit_logs responses so auditors can reconstruct the fragmentation-defense decision without re-deriving it from the full outcome stream. The field’s shape (units, currency, window-boundary reporting) is governance-agent-specific in 3.x and will be standardized in a later revision; implementers exposing it SHOULD document their shape alongside their get_plan_audit_logs response. Governance agents MAY evaluate narrower aggregation scopes additionally (per-brand, per-campaign) but MUST NOT evaluate any scope broader than the declared window without operator sign-off. “Broader” covers both dimensions: a longer trailing window (time) and a wider key tuple (e.g., collapsing across account_id so two accounts share an aggregate). A silently-widened scope in either dimension is as surprising to the operator as a silently-narrowed one.

Composition with `reallocation_threshold`

A reallocation is itself a spend-commit: update_media_buy carries an incremental commit delta (new committed budget − previously committed), and that delta enters the aggregate and counts toward reallocation_threshold evaluation. A buyer cannot sidestep a $25,000 reallocation threshold by fragmenting one $30,000 reallocation into six $4,999 updates — each update’s delta accumulates into the trailing-window aggregate and trips the threshold once the cumulative delta crosses it.

Conformance example

Agent declares aggregation_window_days: 30. Plan sets a human_review_required trigger at $10,000 committed spend (keyed on (buyer_agent, seller_agent, account_id)).

#	Prior 30-day aggregate	Incoming commit	Post-aggregate	Expected outcome
1	$4,000	$2,500 `create_media_buy`	$6,500	Auto-approve — below threshold
2	$8,000	$2,500 `create_media_buy`	$10,500	Escalate to human review — aggregate crosses threshold even though the incoming commit is $2,500 in isolation

A governance agent that approves row 2 without escalation is non-conformant: it has either failed to aggregate across the 30-day window, failed to key on the correct tuple, or failed to include the incoming commit in the sum.

Governance aggregation capability

Sellers and governance agents declare their aggregation window via governance.aggregation_window_days in get_adcp_capabilities. Buyers that depend on a specific window for compliance (e.g., a brand-level weekly cadence review) MUST check this capability before relying on aggregation semantics — a governance agent declaring aggregation_window_days: 7 does not defend against fragmentation spread across a 30-day quarter-end push. Absent declaration means the agent has not committed to any aggregation window and buyers MUST assume per-commit evaluation only (the fragmentation attack surface is open). There is no schema default: omission is not equivalent to a declared 30-day window.

Brand compliance configuration

Compliance policies live at the brand level, not in individual campaign plans. The brand’s policy team configures the brand’s compliance profile, and the governance agent resolves it when processing plans for that brand.

The schema and hosting mechanism for brand compliance configuration are under development by the AgenticAdvertising.org Governance Working Group. The following describes the conceptual model; implementations may vary.

A brand’s compliance configuration contains two kinds of policies:

Registry policies: References to standardized policies in the AdCP policy registry, identified by ID. Each reference MAY include configuration parameters that customize the policy for the brand.
Custom policies: Brand-specific rules expressed as natural language strings, evaluated by the governance agent using the same approach as prompt-based policies.

The policy team selects registry policies that apply to the brand, configures parameters where needed, and adds any custom policies specific to the brand. The buying team never interacts with this configuration — they create campaign plans that reference the brand, and the governance agent resolves applicable policies automatically. The brand’s industries inform automatic policy matching — for example, a brand in the beverage industry would receive any registry policies tagged for that industry.

Policy registry

The policy registry is a community-maintained library of standardized, machine-readable advertising compliance policies. Brands reference policies by ID rather than writing their own. The registry covers three categories:

Category	Examples
Jurisdiction	UK HFSS restrictions, US COPPA, EU GDPR age-gating, California AI disclosure (SB 942)
Vertical	Alcohol age verification, pharma fair balance, gambling self-exclusion, financial services APR disclosure
Brand safety	Brand safety baselines, content suitability tiers

Each policy in the registry has an ID, applicable jurisdictions, a description, and machine-readable rules that governance agents can evaluate programmatically. Policies are versioned as regulations change; brand references MAY pin a specific version, and unversioned references resolve to the current version. The registry format and hosting mechanism are under development by the AgenticAdvertising.org Governance Working Group. This model follows the pattern established by IEEE 7012 (Machine Readable Personal Privacy Terms), which maintains a neutral roster of standardized agreements that parties reference rather than draft individually.

Policy resolution

Policies are declared directly on the plan via policy_ids and custom_policies. When a plan is synced, the governance agent resolves the active policy set:

Load registry policies referenced by policy_ids
Intersect with the plan’s countries and regions — only policies applicable to the plan’s markets are active
Include all custom_policies (these apply regardless of geography)

custom_policies are additive only. Governance agents MUST pin registry-sourced policy text as system-level instructions and MUST NOT permit custom_policies (or the plan’s objectives field) to relax, override, or disable registry-sourced policies. Custom policies may add tighter restrictions — they cannot lower enforcement levels or exempt categories. A custom_policies entry that contradicts a registry policy is evaluated alongside it, not instead of it; the stricter constraint governs.

The plan’s countries and regions fields also serve as geo enforcement: the governance agent MUST reject governed actions targeting markets outside the plan’s allowed geography. A plan with regions: ["US-MA"] rejects actions not explicitly targeting Massachusetts, even if they are otherwise compliant. These fields use the same ISO codes and semantics as product-filters, offerings, and create_media_buy. The resolved policy set is what the governance agent evaluates during check_governance. For the brand_policy and regulatory_compliance categories, the governance agent validates against this resolved set. If the plan has no policy_ids or custom_policies, the governance agent operates with an empty policy set for policy-based categories. Other categories (budget_authority, strategic_alignment, etc.) still apply based on the plan’s parameters.

Audience governance

Campaign plans declare audience targeting constraints, restricted attributes, and policy categories. The governance agent uses these to validate that seller targeting complies with regulatory requirements and campaign intent.

Three-layer model

Audience governance separates three concerns:

Layer	Field	Purpose	Example
Identity	`brand.industries`	What the company does	`["pharmaceuticals", "consumer_packaged_goods"]`
Regulatory regime	`plan.policy_categories`	What regulations apply to this campaign	`["pharmaceutical_advertising", "health_wellness"]`
Data restrictions	`plan.restricted_attributes`	What personal data is off-limits for targeting	`["health_data"]`

A pharmaceutical company is always pharma (identity), but a general awareness campaign might not trigger pharmaceutical advertising regulations (regime), and only campaigns in EU jurisdictions might restrict health data targeting (restrictions).

Audience constraints

Plans can declare audience.include and audience.exclude arrays using audience selectors. Each selector is either a signal reference (pointing to a specific data provider signal) or a natural language description. The governance agent evaluates these constraints against seller targeting in check_governance:

Compare planned_delivery.audience_targeting against the plan’s audience.include/exclude
Compare planned_delivery.audience_targeting against the same constraints (for committed checks)
Detect divergence between what the orchestrator requested and what the seller will activate

Structural governance matching

Signal definitions can self-declare restricted_attributes and policy_categories. When they do, the governance agent performs structural matching — a set intersection between the plan’s restrictions and the signal’s declarations. This is deterministic and requires no LLM inference. For signals without declared governance metadata, the governance agent falls back to semantic matching — inferring sensitivity from the signal name and description. Structural matching produces higher-confidence findings than semantic matching. Restricted attributes apply to both include and exclude targeting. Using restricted data to exclude an audience (e.g., excluding people with health conditions from pharmaceutical ads) is as prohibited as using it for inclusion — both constitute use of restricted personal data for targeting decisions.

Audience distribution drift

During delivery, sellers report audience_distribution in delivery_metrics. Index values indicate demographic composition relative to a declared baseline (census, platform, or custom). A value of 1.0 means parity; values significantly above or below indicate skew. The governance agent tracks both per-period indices and cumulative indices across all reporting periods. This enables detection of systematic bias that might not be visible in any single reporting period.

State tracking

The governance agent tracks state at two levels:

Plan level: Total budget committed, channel allocation percentages, plan status
Campaign level: Per-governance_context committed budget, active media buy references, validation history

A single plan can span multiple campaigns. When check_governance checks budget authority, it considers all campaigns tied to the plan. When report_plan_outcome reports a seller confirmation, the governance agent commits the budget from the seller’s actual amount — not the requested amount.

Plan status

Status	Meaning
`active`	Accepting validation requests and outcome reports
`suspended`	Paused pending human review of a critical escalation
`completed`	Plan finished; read-only

When status is suspended, the governance agent MUST reject all check_governance and report_plan_outcome requests with a CAMPAIGN_SUSPENDED error until the escalation is resolved.

Budget tracking

Budget is committed based on confirmed outcomes, not validated actions. The flow:

check_governance with tool + payload (intent check) checks whether the proposed spend fits within the plan. No budget is committed yet.
The orchestrator executes the action with the seller.
report_plan_outcome reports the seller’s confirmed amount. The governance agent commits this amount to the plan budget.

This ensures budget tracking reflects reality. If a seller reduces the budget from $150K to $120K, the governance agent commits $120K and returns findings about the discrepancy. If the action fails entirely, the governance agent commits $0. An execution check approval validates the seller’s planned delivery against the plan but does not commit budget. Budget is only committed when the orchestrator calls report_plan_outcome with the seller’s confirmed response. Budget checks are point-in-time: check_governance validates against the current committed total but does not reserve budget. If multiple agents execute concurrently against the same plan, two checks could both pass and the combined outcomes could exceed the authorized budget. The governance agent detects overspend at outcome reporting time and returns a budget_authority finding. To prevent concurrent overspend, use delegations with per-agent budget_limit to partition the budget across executing agents.

Drift detection

The audit log includes drift_metrics that surface aggregate governance trends over the plan’s lifetime:

{
  "summary": {
    "checks_performed": 847,
    "drift_metrics": {
      "human_review_rate": 0.03,
      "human_review_rate_trend": "declining",
      "auto_approval_rate": 0.91,
      "human_override_rate": 0.02,
      "mean_confidence": 0.88
    }
  }
}

These metrics detect oversight drift — the gradual migration of control away from humans. A declining human review rate may indicate the governance agent is well-calibrated, or it may indicate that oversight is eroding. Surfacing the trend lets the organization make that judgment.

Metric	What it measures
`human_review_rate`	Fraction of checks that required internal human review
`human_review_rate_trend`	Direction over the plan’s lifetime (`increasing`, `stable`, `declining`)
`auto_approval_rate`	Fraction of checks approved without human intervention
`human_override_rate`	Fraction of human reviews where the human overrode the governance agent
`mean_confidence`	Average confidence score across findings (when confidence is reported)

Organizations can set thresholds on drift metrics. When a metric crosses its threshold, the governance agent SHOULD include a finding (severity warning) on the next governance check:

{
  "drift_metrics": {
    "human_review_rate": 0.01,
    "human_review_rate_trend": "declining",
    "auto_approval_rate": 0.97,
    "thresholds": {
      "human_review_rate_min": 0.02,
      "auto_approval_rate_max": 0.95
    }
  }
}

In this example, both thresholds are breached — the human review rate (0.01) is below the minimum (0.02) and the auto-approval rate (0.97) exceeds the maximum (0.95). This could indicate that the governance agent is approving too broadly, or that policies are well-calibrated for a low-risk campaign. The threshold breach surfaces the question; the organization decides the answer. Organizations set only the thresholds relevant to their concern. A human_review_rate_min catches oversight erosion; a human_review_rate_max catches policy miscalibration. A human_override_rate_max catches a governance agent whose recommendations are consistently wrong. All threshold fields are optional.

Plan amendments

Calling sync_plans with an existing plan_id updates the plan (upsert). The governance agent increments plan_version and applies the new parameters immediately. Active media buys that were approved under the previous plan version are not automatically re-validated — the governance agent evaluates them against the updated plan on the next check_governance call (e.g., the next delivery check). If an amendment reduces the budget below the currently committed amount, the governance agent flags this as a finding on the next governance check.

Validation logic

The governance agent evaluates each validation category independently:

If any category has status failed and the failure is correctable, the status is conditions with suggested fixes
If any category has status failed and the failure is not correctable by the caller, the status is denied
If all categories pass but the overall risk profile warrants human review, the governance agent handles the review internally (the task goes async) and eventually resolves to approved or denied
If all categories pass, the status is approved

The conditions array is only present when the status is conditions. Each condition identifies a specific field, its current value, a suggested value, and the reason for the change.

Finding confidence

Governance findings include an optional confidence score (0-1) and uncertainty_reason that distinguish certain violations from ambiguous ones:

{
  "category_id": "regulatory_compliance",
  "severity": "critical",
  "confidence": 0.85,
  "uncertainty_reason": "Targeting includes 'New Mexico' which partially overlaps LATAM HFSS jurisdiction boundaries",
  "explanation": "Potential HFSS jurisdiction violation based on targeting geography."
}

Confidence informs the appropriate response:

High confidence (0.9+): The finding is definitive. A GDPR violation on a campaign explicitly targeting EU users.
Medium confidence (0.6-0.9): The finding depends on context the governance agent cannot fully resolve. Audience segments that may include minors, geo targeting that partially overlaps regulated jurisdictions.
Low confidence (below 0.6): The finding is speculative. The governance agent flags it for human review rather than acting on it autonomously.

Without confidence, every finding is presented as equally certain, which either over-blocks (if treated as certain) or trains people to ignore findings (if many are false positives). Governance agents SHOULD include confidence when the evaluation involves natural language interpretation or probabilistic matching.

Phase inference

The governance agent infers the validation phase from the tool parameter in check_governance:

tool	Phase
`get_products`	Discovery — validates search intent, seller eligibility, product suitability
`create_media_buy`	Purchase — validates budget authority, targeting compliance, flight dates
`update_media_buy`	Purchase — validates change magnitude, reallocation thresholds
`acquire_rights`	Purchase — validates budget authority, geo compliance, flight dates
`update_rights`	Purchase — validates change magnitude, reallocation thresholds
`activate_signal`	Purchase — validates budget authority, geo compliance, flight dates
`build_creative`	Purchase — validates budget authority, geo compliance

Phase context is cumulative. During purchase, the governance agent considers what was discovered during discovery. The check_id returned by check_governance is used by report_plan_outcome to link the seller’s response back to the validated action.

Capability declaration

Governance agents declare their Campaign Governance support in get_adcp_capabilities:

{
  "governance": {
    "campaign_governance": {
      "categories": [
        {
          "category_id": "budget_authority",
          "description": "Validates spend against plan budget limits and allocation rules."
        },
        {
          "category_id": "strategic_alignment",
          "description": "Validates that purchases match campaign brief and channel mix targets."
        },
        {
          "category_id": "bias_fairness",
          "description": "Checks targeting for discriminatory patterns and protected category compliance.",
          "jurisdictions": ["US", "EU", "UK"]
        },
        {
          "category_id": "regulatory_compliance",
          "description": "Validates jurisdiction-specific advertising regulations.",
          "jurisdictions": ["US", "EU", "UK"]
        },
        {
          "category_id": "seller_verification",
          "description": "Compares seller setup against original requests to detect discrepancies."
        },
        {
          "category_id": "brand_policy",
          "description": "Enforces brand-level compliance policies resolved from the brand configuration and policy registry."
        }
      ]
    }
  }
}

Integration with `create_media_buy`

The buyer includes plan_id on the create_media_buy request and governance_context on the protocol envelope. These fields tell the seller which governance plan applies, enabling seller-side governance checks.

{
  "tool": "create_media_buy",
  "arguments": {
    "plan_id": "plan_q1_2026_launch",
    "account": { "agent_url": "https://seller.example.com", "id": "acc_123" },
    "brand": { "domain": "acmecorp.com" },
    "start_time": "2026-03-15T00:00:00Z",
    "end_time": "2026-06-15T00:00:00Z",
    "packages": ["..."]
  }
}

The seller’s response includes planned_delivery — what the seller will actually run:

{
  "seller_reference": "mb_seller_456",
  "packages": ["..."],
  "planned_delivery": {
    "geo": { "countries": ["US"] },
    "channels": ["olv"],
    "start_time": "2026-03-15T00:00:00Z",
    "end_time": "2026-06-15T00:00:00Z",
    "total_budget": 150000,
    "currency": "USD",
    "frequency_cap": { "max_impressions": 3, "per": "user", "window": { "interval": 1, "unit": "days" } },
    "audience_summary": "Adults 25-54, US, premium video inventory",
    "enforced_policies": ["us_coppa"]
  }
}

planned_delivery is the seller’s interpretation of the request — the actual delivery parameters they will use. It serves two purposes:

Governance checks — When the account has a governance agent configured, the seller sends planned_delivery to the governance agent for verification before confirming the media buy.
Transparency — The buyer can compare planned_delivery against what they requested to catch discrepancies early, before delivery begins.

Governance checks

Campaign Governance’s buyer-side validation has a trust limitation: the buyer’s orchestrator grades its own homework. An LLM agent could hallucinate governance approval, skip validation, or misrepresent what was validated. Seller-side governance checks close this gap by giving sellers an independent way to confirm that purchases are approved. The seller POSTs to the buyer’s configured governance agent URL when governed action events occur. The governance agent maintains all state and correlates requests by plan_id + governance_context — the seller does not need to track governance history or chain IDs across calls.

Both checks must pass

Every governed action MUST pass both the buyer-side intent check and the seller-side planned-delivery check. Both calls hit the same authority (the buyer’s governance agent), so there is no “two agents disagreeing” case — but the invariant that both calls must succeed is load-bearing:

The buyer-side intent check confirms the plan permits the spend in principle.
The seller-side planned-delivery check confirms the seller’s actual delivery parameters are consistent with the approved plan.

These are not redundant. A buyer’s intent check can pass (the plan allows $100K on premium video) while the seller’s planned-delivery check fails (the seller’s planned line-up includes inventory the plan excludes). If either returns denied, the action MUST NOT proceed. When both return approved with non-empty conditions, the applied set is the union of both responses’ conditions. Contradictory conditions (one side requires X, the other requires NOT X) resolve to denied with a structured finding rather than silent precedence. A seller that rejects a trade for its own content-standards or commercial reasons is not participating in a governance conflict — that is a separate, commerce-layer rejection (e.g., TERMS_REJECTED) and follows the regular rejection path. Governance speaks only for the buyer’s plan.

Setup

The buyer syncs governance agents via sync_governance, pairing each account with the governance agent endpoints to call. Each agent includes authentication credentials so the governance agent can verify the seller’s identity:

{
  "tool": "sync_governance",
  "arguments": {
    "accounts": [
      {
        "account": {
          "brand": { "domain": "acmecorp.com" },
          "operator": "pinnacle-media.com"
        },
        "governance_agents": [
          {
            "url": "https://governance.pinnacle-media.com",
            "authentication": {
              "schemes": ["Bearer"],
              "credentials": "gov_token_acme_pinnacle_2026_xyzxyzxyz..."
            }
          }
        ]
      }
    ]
  }
}

The seller stores these endpoints and presents the credentials when calling check_governance. The governance agent MUST verify that the Bearer token matches a registered credential for the account associated with the plan_id, and MUST reject requests with unrecognized or mismatched credentials.

Governance modes

Governance mode (audit, advisory, enforce) is an internal implementation detail of the governance agent, not a protocol-level field. The caller sends check_governance and receives approved, denied, or conditions — it does not need to know what mode produced that decision. This means:

A governance agent in audit mode internally always returns approved with findings attached
A governance agent in advisory mode internally may return denied but the organization treats it as non-blocking
A governance agent in enforce mode returns denied and expects the caller to stop

Mode is configured by the buyer’s policy team on the governance agent itself, not via the protocol. The governance agent MAY include mode information in its audit logs or get_plan_audit_logs response for post-hoc analysis, but callers MUST NOT branch behavior based on mode — they act on the status they receive. See the safety model for the crawl-walk-run adoption path.

Governance context

The governance_context field is an opaque string issued by the governance agent in the check_governance response. It correlates any governed action’s lifecycle and is the primary audit/reporting key. The governance agent encodes whatever internal state it needs (plan reference, budget snapshot, check history) into this value. Callers MUST NOT interpret governance_context. They persist and forward it:

Buyer: receives governance_context from check_governance response, attaches it to the protocol envelope when sending the media buy to the seller.
Seller: receives governance_context in the envelope, stores it alongside the media buy, and includes it on all subsequent check_governance calls for that media buy’s lifecycle.
Governance agent: uses governance_context to reconnect each lifecycle event to the original plan, campaign grouping, and budget state.

On the first check_governance call (before any context exists), the governance agent extracts what it needs from payload and plan_id. On subsequent calls, governance_context provides the continuity so the governance agent does not need to re-derive state from the payload. Governance agents SHOULD treat governance_context as a lookup key into server-side state or a signed token, not as a plain-text encoding of governance state. If state is encoded directly, it MUST be signed (e.g., HMAC) so tampering by intermediaries is detectable. In AdCP 3.0 the encoded value is a compact JWS signed per the AdCP JWS profile. The token carries a required plan_hash claim that binds the attestation to the exact plan state the governance agent evaluated (see Plan binding and audit below). Callers still treat the value as opaque for correlation; sellers that opt into verification follow the seller verification checklist, which verifies the token’s authenticity, authorization scope, and freshness — not the buyer’s plan.

Plan binding and audit

The plan_hash claim is the cryptographic receipt that forever binds a signed governance_context attestation to the exact plan state the governance agent evaluated. It is an audit-layer property — sellers do not verify it and are not expected to. The buyer’s plan carries commercially sensitive data (cross-seller allocations, per-seller caps, objectives, approved_sellers lists, custom policies, ext) that buyers do not share with sellers; there is no plan-retrieval mechanism in 3.x and none is planned. plan_hash rides inside the JWS because the JWS is already the signed artifact the governance agent produces, but it is not part of the wire-verification contract. What the claim delivers:

Post-hoc accountability. Every governance attestation is forever-bindable to the plan state it attested to. Regulators and forensic audits can prove “this transaction was authorized under plan state X at time T” years after the fact, using only the retained JWS and the governance agent’s revision records.
Governance-agent self-integrity. On every check_governance call, the governance agent re-evaluates current plan state and re-hashes. Tampering with the governance agent’s persisted plan between calls surfaces as a mismatch against the retained revision record.
Buyer-side compliance verification. A buyer’s own tooling can verify its governance agent is producing tokens that match the plan the buyer actually pushed — catching a compromised or misbehaving governance vendor.

Canonicalization

plan_hash = base64url_no_pad(SHA-256(JCS(plan_payload))) where:

JCS is RFC 8785 JSON Canonicalization Scheme — the same scheme used for idempotency payload equivalence. Governance agents and auditor verifiers SHOULD use the same library implementations listed there. JCS sorts object keys lexicographically by code point (caller key order in the sync_plans request does not affect the hash) and preserves the distinction between an omitted optional field and an explicit null (these produce different hashes). Governance agents MUST hash the plan as-supplied; they MUST NOT synthesize omitted optionals to default values and MUST NOT drop explicit nulls.
plan_payload is one element of the plans[] array as supplied on sync_plans — a single plan object, not the sync_plans request envelope and not the plans wrapper array. The preimage is the current plan-revision state at the time of attestation, i.e., the plan object the governance agent just evaluated. Implementations constructing the preimage start from the plan-revision object and remove the closed set of bookkeeping fields listed below.
base64url_no_pad follows RFC 4648 §5 with trailing = padding stripped — consistent with jti and other base64url values in the JWS profile. Governance agents MUST emit the unpadded form. Verifiers (governance agents re-verifying their own tokens, auditors, buyer-side compliance tooling) MUST compare by base64url-decoding both sides to the raw 32-byte SHA-256 digest and comparing bytes — NOT by string equality of the encoded form — so padding, case, or alphabet variation is rejected as a decode failure rather than producing a false non-match. A plan_hash that does not decode to exactly 32 bytes MUST be rejected.

Excluded fields

Closed list — governance agents MUST NOT extend or shrink it; any addition is a breaking change requiring a profile version bump, same rule as Payload equivalence:

version — governance-agent revision counter, set by the agent on each re-sync
status — plan lifecycle status managed by the agent
syncedAt — timestamp written on each re-sync
revisionHistory — agent-internal append-only revision log (MUST be append-only archival; implementations MUST NOT read revisionHistory entries back into the active plan-object shape used for hashing)
committedBudget — derived from downstream check_governance / report_plan_outcome activity
committedByType — derived from the same activity

None of these appear in the sync_plans request schema (additionalProperties: false on the plan item); they exist only on the governance agent’s persisted plan state. The list is stated explicitly so an implementer naively hashing their internal state struct strips the right fields. Implementations that discover additional GA-internal fields on their persisted plan state beyond this list MUST treat those fields as IN the preimage until the profile version bumps — fail-safe toward inclusion, not exclusion. The “anything that looks like bookkeeping → strip” shortcut silently diverges every implementation that guesses differently; the safe default is “if it is not on the closed list, it is part of the hash.” All other fields — including ext, custom_policies, objectives, delegations, human_override, and every field declared in the sync_plans plan-item schema — are IN the preimage. Caller guidance:

ext is part of the preimage. Buyers MUST NOT place rotating tokens or retry-unstable values inside ext on a plan; a value that changes between re-syncs invalidates every outstanding governance_context token for the plan even when the buyer’s declared intent is unchanged (consistent with idempotency’s treatment of ext).
delegations[].expires_at is declared intent and SHOULD be stable across re-syncs of the same plan; regenerating it from “now + N days” on every sync causes hash churn.
Array order in policy_ids, policy_categories, custom_policies, approved_sellers, delegations, countries, regions, channels.required, channels.allowed is not semantically meaningful, but JCS preserves it. Buyers SHOULD emit these in stable order across re-syncs.
JCS does not Unicode-normalize. Per RFC 8785 §3.2.5, JCS preserves strings as-supplied — visually-indistinguishable Unicode variants (Latin a vs Cyrillic а, NFC-vs-NFD compositions, confusable homoglyphs) produce distinct bytes and therefore distinct hashes. plan_hash detects this divergence correctly at the cryptographic layer, but the plan-semantics layer does not: two plans whose policy_ids or policy_categories differ only by a homoglyph substitution authorize different enforcement outcomes at the governance agent and at downstream consumers. Buyers and governance agents SHOULD validate policy_ids and policy_categories against a canonical allowlist server-side before emitting or evaluating them. This is a plan-content rule, not a hashing rule — plan_hash integrity is intact either way; the allowlist is what prevents a homograph substitution from producing a different authorization decision.

Governance-agent obligations

Governance agents MUST:

Compute plan_hash over the current plan state on every check_governance call and include it in the signed JWS payload. The hash MUST be over the plan the agent just evaluated; stale attestations over mutated plans MUST NOT be produced.
Refresh the signature on every check_governance invocation — fresh jti, iat, exp, and plan_hash. Governance agents MUST NOT cache and re-emit a previously-signed governance_context token across plan revisions. Envelope idempotency response caching is a separate regime — governance_context is on the closed exclusion list in Payload equivalence precisely so it can rotate on replay.
Retain the per-revision plan_hash alongside each internal plan-revision record — MUST, regardless of whether audit_log_pointer is exposed. Retention is what delivers the forever-binding property; without universal retention, every governance agent that doesn’t use audit_log_pointer silently voids the audit layer for its entire token corpus. An audit log that cannot be joined back to the attested plan state is half an audit trail, and a governance agent that cannot verify its own historical tokens cannot detect tampering of its own store. Retained values are implementation-internal and are never exposed on the wire except through the normalized response on get_plan_audit_logs, which echoes plan_hash per entry so auditors do not have to reconstruct from the governance agent’s private records.

Wire-verification contract

plan_hash is not listed in crit. crit is wire-verifier semantics (RFC 7515 §4.1.11): it forces verifiers to reject tokens whose listed claims they cannot process. No wire verifier processes plan_hash — the only parties who can fetch the preimage (governance agent, auditor, buyer compliance) are off-wire. Listing in crit would force sellers to reject tokens they have no basis to verify, with no offsetting benefit. Governance agents MUST emit the claim and MUST NOT list it in crit. Sellers persist and forward governance_context verbatim and perform the 15-step JWS verification checklist — authenticity, authorization scope, freshness. They treat plan_hash as opaque cargo inside the token and never inspect it.

Verification recipes

Auditor recipe. A regulator or third-party auditor with access to a plan’s audit logs verifies historical attestations as follows:

Call get_plan_audit_logs with include_entries: true to retrieve the audit trail. Each check entry carries plan_hash (the claim asserted at issuance time) and governance_context (the signed JWS).
For each governance_context, decode the compact JWS and verify the 15-step JWS contract (signature, iss in brand.json, aud, exp, etc.) against the governance agent’s published JWKS.
Extract the plan_hash claim from the decoded JWS payload and base64url-decode to 32 raw bytes.
Decode the entry-level plan_hash to 32 raw bytes and byte-compare to the claim. A mismatch means the retained audit record disagrees with the signed token — either the token was tampered with or the record was, and the governance agent’s integrity is in question.
Optionally: recompute plan_hash from the governance agent’s retained per-revision plan record (if the auditor has authenticated access to the governance agent’s revision store) and byte-compare again. A mismatch here means the governance agent’s own store was tampered with between signing and audit.

Buyer-side compliance recipe. A buyer whose own tooling wants to verify its governance agent is producing honest tokens:

Observe governance_context tokens flowing through the protocol envelope to sellers (the buyer already has these; no retrieval needed).
For each token, decode the JWS, extract the plan_hash claim, and base64url-decode to 32 bytes.
Recompute plan_hash over the buyer’s own copy of the plan at the revision the token attests to. The buyer has authoritative plan state from their own sync_plans calls.
Byte-compare. A mismatch means the governance agent is signing attestations that don’t match the plan the buyer actually pushed — either vendor compromise or a bug. Either is a critical finding the buyer should escalate.

This path catches a misbehaving governance vendor without involving the seller, the auditor, or the protocol. The data is already on the buyer’s side. Constant-time comparison. All three verifier types — governance-agent self-integrity, auditor, and buyer-side compliance — SHOULD use constant-time byte comparison (e.g., crypto.timingSafeEqual in Node, hmac.compare_digest in Python, crypto/subtle.ConstantTimeCompare in Go) when comparing plan_hash digests. Timing side channels on a SHA-256-length comparison are not practically exploitable today; the rule is cheap insurance against future deployments that re-use the comparison code on shorter digests or against adversaries with co-tenancy on the verifier host.

Privacy considerations

plan_hash changes across re-syncs reveal that the plan mutated, even without revealing what mutated. Parties retaining tokens long-term (sellers forwarding governance_context verbatim, auditors, regulators) can infer plan-mutation cadence from the sequence of distinct hashes they observe for a given plan_id. For most deployments this is acceptable or desired — mutation-cadence is part of the audit signal. Sensitive governance deployments where mutation frequency itself is commercially or operationally sensitive (e.g., a buyer does not want a seller to infer re-planning cadence across its campaign portfolio) SHOULD factor this into token-retention policy: shorter seller-side retention windows for governance_context, or periodic rotation of the plan’s jti namespace to break cross-token linkability.

Reference test vectors

Eleven vectors under static/compliance/source/test-vectors/plan-hash/ pin the canonicalization bit-exactly: a minimal plan, a plan exercising every optional field, a bookkeeping-stripped case (GA-internal fields present on the stored plan but stripped before hashing, yielding the same hash as the bookkeeping-absent equivalent), paired vectors proving that omitted-vs-explicit-null, array order in policy_categories, and rotating ext.trace_id all produce distinct hashes, a Unicode case confirming JCS does not normalize per RFC 8785 §3.2.5, and a numeric-canonicalization case with fractional percentages that pins library choice over hand-rolled JSON.stringify + key sort. Each vector records the preimage, the canonical JCS bytes, the SHA-256 hex digest, and the final plan_hash claim value. Governance agents and auditor verifiers MUST reproduce these hashes bit-exactly.

Governance phases

Governance checks cover the full media buy lifecycle through three phases:

Phase	Trigger	What’s validated
`purchase`	`create_media_buy`, `acquire_rights`, `activate_signal`, `build_creative`	Budget, geo, channels, flight dates, policies
`modification`	`update_media_buy`, `update_rights`	Change magnitude, reallocation, new parameters
`delivery`	Periodic (seller-initiated)	Pacing, spend rate, geo drift, channel distribution

The phase field defaults to purchase if omitted, so existing implementations continue to work without changes. The governance agent maintains all state and correlates requests by plan_id + governance_context. The seller does not chain check IDs or track conversation history — it posts what happened, and the governance agent looks up context.

Purchase phase

When the seller receives a create_media_buy request on an account with governance_agents:

The seller interprets the request and determines its planned_delivery.
The seller calls check_governance with phase: "purchase", the plan_id, and planned_delivery.
The governance agent validates the planned delivery against the campaign plan.
If approved, the seller confirms the media buy.
If denied, the seller rejects the media buy with an GOVERNANCE_DENIED error.
If conditions, the seller adjusts its planned delivery to meet the conditions and re-verifies, or rejects.

Modification phase

When the seller receives an update_media_buy request:

The seller interprets the update and determines the new planned_delivery.
The seller calls check_governance with phase: "modification", the updated planned_delivery, and a modification_summary.
The governance agent looks up the governed action by plan_id + governance_context and evaluates the changes against the plan.
If approved, the seller confirms the update.
If denied or conditions, the seller follows the same flow as purchase phase.

The governance agent can apply different logic to modifications than to initial purchases. For example, a small budget increase within reallocation_threshold might be auto-approved, while a large budget increase or new geo market might require stricter scrutiny.

Delivery phase

The seller calls check_governance with phase: "delivery" periodically during active delivery. This creates a direct reporting channel between the seller and the buyer’s governance agent.

The seller collects delivery metrics for the reporting period.
The seller calls check_governance with phase: "delivery", the current planned_delivery, and delivery_metrics.
If approved, the response includes next_check — when the seller should report again.
If denied, the seller pauses delivery immediately.
If conditions, the seller adjusts delivery (e.g., slow pacing, shift geo targeting) and re-verifies immediately.

The governance agent opts in to delivery reporting by including next_check in the purchase approval response. If the purchase response has no next_check, the governance agent does not expect delivery reports. The governance agent controls the reporting cadence through next_check. It can tighten the cadence (shorter intervals) when it detects drift or conditions, and relax it (longer intervals) when delivery is stable. The governance agent MAY treat a missed next_check deadline as a finding on the next delivery check.

Verification examples

Purchase request:

{
  "tool": "check_governance",
  "arguments": {
    "plan_id": "plan_q1_2026_launch",
    "caller": "https://seller.example.com",
    "governance_context": "gc_from_buyer_envelope",
    "phase": "purchase",
    "planned_delivery": {
      "geo": { "countries": ["US"] },
      "channels": ["olv"],
      "start_time": "2026-03-15T00:00:00Z",
      "end_time": "2026-06-15T00:00:00Z",
      "total_budget": 150000,
      "currency": "USD",
      "frequency_cap": { "max_impressions": 3, "per": "user", "window": { "interval": 1, "unit": "days" } },
      "audience_summary": "Adults 25-54, US, premium video inventory",
      "enforced_policies": ["us_coppa"]
    }
  }
}

Authorized (purchase with delivery opt-in):

{
  "check_id": "auth_001",
  "verdict": "approved",
  "plan_id": "plan_q1_2026_launch",
  "explanation": "Planned delivery is within plan parameters. Budget: $150,000 of $500,000 plan total. Geo: US (within plan). Channel: OLV (within 40-70% target range).",
  "expires_at": "2026-03-15T01:00:00Z",
  "next_check": "2026-03-22T00:00:00Z"
}

The next_check field signals that the governance agent expects delivery reporting. If absent, no delivery reports are expected. Denied (purchase):

{
  "check_id": "auth_002",
  "verdict": "denied",
  "plan_id": "plan_q1_2026_launch",
  "explanation": "Planned delivery targets CA (Canada) which is not an authorized market for this plan.",
  "findings": [
    {
      "category_id": "strategic_alignment",
      "severity": "critical",
      "explanation": "Geo targeting includes CA but plan only authorizes US.",
      "details": {
        "plan_countries": ["US"],
        "planned_countries": ["US", "CA"]
      }
    }
  ]
}

Authorized (delivery):

{
  "check_id": "auth_004",
  "verdict": "approved",
  "plan_id": "plan_q1_2026_launch",
  "explanation": "Delivery on track. Week 1 spend: $12,500 of $150,000 (8.3%). Pacing is on target for 13-week flight.",
  "next_check": "2026-03-29T00:00:00Z"
}

Enforcement

When governance_agents is present on the account, the seller MUST call check_governance before confirming any media buy. The buyer provided the endpoints specifically so that purchases are independently verified — skipping it defeats the purpose. When governance_agents is absent, the seller processes media buy requests normally. The buyer-side governance loop (intent check -> execute -> report_plan_outcome) still applies, but there is no seller-side verification. Sellers MUST NOT require governance checks as a prerequisite for all accounts. A seller that refuses to process media buys from accounts without governance_agents would break interoperability with buyers who do not use Campaign Governance. The delivery phase is optional even when purchase phase governance is used. A seller MAY support purchase approval without ongoing delivery reporting. The governance agent indicates whether it expects delivery reports through the presence of next_check in the purchase response. If the governance agent is unreachable (timeout, network error), the seller MUST NOT proceed with the media buy. Governance checks are a prerequisite for confirming purchases on accounts with registered governance_agents. The seller SHOULD retry the check after a brief delay and reject the media buy with a GOVERNANCE_UNAVAILABLE error if the agent remains unreachable. When the orchestrator receives GOVERNANCE_UNAVAILABLE from a seller, it SHOULD retry the create_media_buy after a delay. If the governance agent remains unavailable, the orchestrator SHOULD escalate to a human rather than attempting alternative sellers — the governance outage affects all sellers on the same account. A prior intent check approval from the orchestrator does not substitute for the seller’s execution check; the seller validates independently and cannot use the orchestrator’s approval.

Performance expectations

Governance agent implementations SHOULD respond to check_governance calls within 5 seconds for intent checks and 10 seconds for execution checks. Sellers SHOULD configure appropriate timeouts and treat timeouts the same as unavailability (retry, then reject with GOVERNANCE_UNAVAILABLE).

Wire format

The seller calls each governance agent at its registered URL using MCP over HTTP (Streamable HTTP transport). The request is an MCP tools/call invocation with tool name check_governance and the request arguments as the tool input. Authentication uses the Bearer token from the agent’s authentication.credentials in the Authorization header.

One governance agent per account

An account binds to exactly one governance agent per sync_governance. Registration is single-agent by schema — governance_agents is an array because that is what 3.0 shipped with, constrained to maxItems: 1 as a load-bearing invariant (not a staging post toward relaxation). The envelope carries a single governance_context token; all lifecycle calls route to that one agent. Loosening the cap would require a coordinated change across sync_governance, the protocol envelope, and every lifecycle task that threads the token — that change is not planned. This is deliberate. A governance plan is unitary — budget authority, delivery monitoring, brand safety, and regulatory compliance are not independent specialisms held by different authorities. They are phases and facets of the same evaluation against the same plan state:

Authorization, fidelity, and drift are phases, not specialisms. check_governance already separates them on the phase axis (purchase / modification / delivery). Splitting them across agents partitions the same plan state across separate authorities, which can only produce drift, disagreement, or duplicated re-reads of the same plan.
Regulatory rules are encoded in the plan, not held by a separate agent. enforced_policies, restricted_attributes, policy_ids, and human_review_required live on the plan itself. A “regulatory compliance” agent separate from the “spend authority” agent would re-evaluate the same plan and reach the same decision, or diverge — neither is useful.
Internal specialist review belongs inside the governance agent. A buyer that wants legal, brand-safety, or category-specialist review composes those reviewers behind a single governance-agent endpoint (human review, internal routing, multi-reviewer consensus are all governance-agent-internal concerns). The protocol sees one agent; the agent’s internal organization is the agent’s business.
One lifecycle, one token, one audit trail. Plan binding (plan_hash), signed context (governance_context), and get_plan_audit_logs are all single-agent designs. A single agent is what makes post-hoc accountability (“this transaction was authorized under plan state X by agent Y at time T”) a clean, verifiable claim.

Buyers that need internal specialist review (legal, brand safety, category) compose those reviewers inside the governance agent they configure — the protocol does not surface the split. Internal decomposition is auditable through check-governance-response.findings[]. Each finding carries category_id (the agent-internal taxonomy — pharma MLR, brand safety, legal compliance, whichever specialism flagged it) and policy_id (the specific policy that triggered the finding). Buyers and sellers see one consolidated decision; per-finding attribution lets readers trace which specialist within the governance agent contributed to a denial or condition without surfacing the split as separate protocol-level agents. When the violation traces to a producer-tagged surface — a feature_requirements[i].policy_id the buyer authored, a creative-feature-result.policy_id the creative agent recorded, or a validation-result.features[i].policy_id a property-list agent emitted — the governance agent echoes that policy_id on the finding for end-to-end traceability. See Policy Attribution for the producer contract. Related specialist reviews that sit adjacent to (not inside) campaign governance — brand-safety pre-screen of creatives, property-list policy, content-standards evaluation — are separate governance surfaces with their own agents and their own lifecycle (see build_creative, property governance, content-standards governance). Campaign governance speaks only for the plan.

Governance checks and the governance loop

Governance checks complement the buyer-side governance loop, they do not replace it:

Concern	Intent checks (orchestrator, `tool` + `payload`)	Execution checks (seller, `governance_context` + `planned_delivery`)
Who checks	Buyer’s governance agent, called by orchestrator	Buyer’s governance agent, called by seller
When	Before the buyer sends the request	Before confirm, on update, during delivery
What’s validated	The buyer’s intended action	The seller’s planned and actual delivery
Trust model	Self-attested	Independently verified
Budget tracking	Yes (plan state)	Governance agent maintains state
Ongoing monitoring	Via `report_plan_outcome`	Via `delivery` phase

The delivery phase gives the governance agent real-time visibility into what sellers are actually delivering. The buyer-side report_plan_outcome depends on the orchestrator reporting honestly; the delivery phase gets reports directly from the seller. The buyer-side and seller-side governance checks hit the same agent — the one registered on the account via sync_governance. The orchestrator calls it for intent checks and the seller calls it for execution checks; both conversations reach the same authority with the same plan state.

Orchestrator integration pattern

The governance check is a synchronous call in the orchestrator’s action loop. The orchestrator calls check_governance with tool + payload (intent check) before sending requests to sellers. Seller-side execution checks are transparent to the orchestrator — the orchestrator sends the same create_media_buy request regardless of whether governance checks are configured. Modification and delivery phase checks happen between the seller and governance agent, independent of the orchestrator’s governance loop.

Audit trail

Every plan maintains an ordered audit trail of all validated actions and reported outcomes, retrievable via get_plan_audit_logs. The trail includes:

Check ID, timestamp, and tool
The status and category evaluations
Outcome status and committed budget
Any findings from outcome reports
Any internal escalations and their resolutions (recorded by the governance agent)
The human approver identity (when human review occurred internally)
Delivery metrics over time

This audit trail serves compliance and reporting needs. For regulated categories (political advertising, financial services), the trail provides evidence that governance was applied to every transaction.

Conformance testing

A conformance test suite for governance agent implementations is planned. Test vectors provide structured input/output pairs — a plan, a set of policies, a check_governance request, and the expected response status and findings. Governance agents can run these vectors to verify that their policy evaluation produces consistent results. The policy registry’s exemplars (pass/fail scenarios per policy) provide the raw material. Test vectors formalize these into executable assertions that any governance agent can validate against. The AdCP client test library will include these vectors as part of its standard test suite.

Property list governance

Campaign governance intersects with property governance when media buys reference property lists. A governance agent MAY validate that property lists referenced in media buy requests meet the plan’s brand safety and compliance requirements, ensuring that property lists align with the brand’s compliance configuration and enforced policies.

Documentation Index

​Campaign Governance specification

​Campaign plan

​Purchase types

​Budget reallocation

​Budget allocations

​Human review required

​Spend-commit invocation

​Spend-commit tasks

​Seller enforcement

​Audit logging

​Interaction with idempotency

​Channel mix targets

​Delegations

​Portfolio governance

​Aggregated-spend evaluation (fragmentation defense)

​Composition with reallocation_threshold

​Conformance example

​Governance aggregation capability

​Brand compliance configuration

​Policy registry

​Policy resolution

​Audience governance

​Three-layer model

​Audience constraints

​Structural governance matching

​Audience distribution drift

​State tracking

​Plan status

​Budget tracking

​Drift detection

​Plan amendments

​Validation logic

​Finding confidence

​Phase inference

​Capability declaration

​Integration with create_media_buy

​Governance checks

​Both checks must pass

​Setup

​Governance modes

​Governance context

​Plan binding and audit

​Canonicalization

​Excluded fields

​Governance-agent obligations

​Wire-verification contract

​Verification recipes

​Privacy considerations

​Reference test vectors

​Governance phases

​Purchase phase

​Modification phase

​Delivery phase

​Verification examples

​Enforcement

​Performance expectations

​Wire format

​One governance agent per account

​Governance checks and the governance loop

​Orchestrator integration pattern

​Audit trail

​Conformance testing

​Property list governance

Campaign Governance specification

Campaign plan

Purchase types

Budget reallocation

Budget allocations

Human review required

Spend-commit invocation

Spend-commit tasks

Seller enforcement

Audit logging

Interaction with idempotency

Channel mix targets

Delegations

Portfolio governance

Aggregated-spend evaluation (fragmentation defense)

Composition with `reallocation_threshold`

Conformance example

Governance aggregation capability

Brand compliance configuration

Policy registry

Policy resolution

Audience governance

Three-layer model

Audience constraints

Structural governance matching

Audience distribution drift

State tracking

Plan status

Budget tracking

Drift detection

Plan amendments

Validation logic

Finding confidence

Phase inference

Capability declaration

Integration with `create_media_buy`

Governance checks

Both checks must pass

Setup

Governance modes

Governance context

Plan binding and audit

Canonicalization

Excluded fields

Governance-agent obligations

Wire-verification contract

Verification recipes

Privacy considerations

Reference test vectors

Governance phases

Purchase phase

Modification phase

Delivery phase

Verification examples

Enforcement

Performance expectations

Wire format

One governance agent per account

Governance checks and the governance loop

Orchestrator integration pattern

Audit trail

Conformance testing

Property list governance