Campaign Governance specification

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",
    "authority_level": "agent_limited",
    "per_seller_max_pct": 40,
    "reallocation_threshold": 25000
  },
  "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": ["No competitor brand adjacency"],
  "approved_sellers": null,
  "ext": {}
}

Budget authority levels

Level	Meaning
`agent_full`	Agent can execute any spend within the total budget without human approval
`agent_limited`	Agent can execute within thresholds but MUST escalate large changes (defined by `reallocation_threshold`)
`human_required`	Every spend commitment requires human approval

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 principal-agent relationship between brand and agency 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.

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", "authority_level": "agent_limited" },
  "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": [
      "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: Natural language exclusion rules applied to all member plans.

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.

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)

The plan’s countries and regions fields also serve as geo enforcement: the governance agent MUST reject media buys targeting markets outside the plan’s allowed geography. A plan with regions: ["US-MA"] rejects buys 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

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:

{
  "media_buy_id": "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 governance_agents, the seller sends planned_delivery to the governance agent(s) 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 governance_agents URLs when media buy events occur. The governance agent maintains all state and correlates requests by plan_id + media_buy_id — the seller does not need to track governance history or chain IDs across calls.

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 enables lifecycle continuity — 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.

Governance phases

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

Phase	Trigger	What’s validated
`purchase`	`create_media_buy`	Budget, geo, channels, flight dates, policies
`modification`	`update_media_buy`	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 + media_buy_id. 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 media buy by plan_id + media_buy_id 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",
    "media_buy_id": "mb_seller_456",
    "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",
  "status": "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",
  "status": "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",
  "status": "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.

Multi-agent composition

Accounts MAY register multiple governance agents via sync_governance, each responsible for different validation categories. For example, one agent handles budget authority and strategic alignment while another handles regulatory compliance and brand policy. When multiple governance agents are registered, the seller MUST call each agent whose categories overlap with the action being validated. All applicable agents must approve for the action to proceed (unanimous approval). If any agent returns denied, the action is blocked. For accounts with a single governance agent, pass a one-element array.

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, `media_buy_id` + `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 MAY be handled by the same agent or by separate agents. The protocol does not prescribe the relationship — only that the seller can call the governance_agents URLs registered on the account.

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

​Campaign Governance specification

​Campaign plan

​Budget authority levels

​Channel mix targets

​Delegations

​Portfolio governance

​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

​Setup

​Governance modes

​Governance context

​Governance phases

​Purchase phase

​Modification phase

​Delivery phase

​Verification examples

​Enforcement

​Performance expectations

​Wire format

​Multi-agent composition

​Governance checks and the governance loop

​Orchestrator integration pattern

​Audit trail

​Conformance testing

​Property list governance

Campaign Governance specification

Campaign plan

Budget authority levels

Channel mix targets

Delegations

Portfolio governance

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

Setup

Governance modes

Governance context

Governance phases

Purchase phase

Modification phase

Delivery phase

Verification examples

Enforcement

Performance expectations

Wire format

Multi-agent composition

Governance checks and the governance loop

Orchestrator integration pattern

Audit trail

Conformance testing

Property list governance