Agentic Intent Taxonomy

Purpose

The Agentic Intent Taxonomy defines a normalized decision envelope for representing conversational intent in an operator-neutral way. It is designed to:

align content classification with IAB Content Taxonomy
separate model prediction from system decision
support monetization gating, policy enforcement, and auditing
remain usable across operators, platforms, exchanges, and advertisers

Canonical IAB reference:

IAB Content Taxonomy 3.1 TSV

Top-Level Structure

The schema is divided into two top-level objects:

Field	Type	Required	Description
`model_output`	object	Yes	Model-produced classification, including IAB mapping, intent, context, and optional fallback metadata
`system_decision`	object	Yes	System-produced policy, opportunity, and optional trajectory output

`model_output`

model_output contains predictive output. It should reflect what the model inferred, not the final monetization decision.

`model_output.classification`

Field	Type	Required	Description
`iab_content`	object	No	IAB-aligned content mapping using either compact tiers or explicit level objects
`intent`	object	Yes	Primary intent classification block
`context`	object	No	Sparse decision-relevant entities and constraints

iab_content should map against the canonical IAB Content Taxonomy 3.1 TSV: https://github.com/InteractiveAdvertisingBureau/Taxonomies/blob/develop/Content%20Taxonomies/Content%20Taxonomy%203.1.tsv

`model_output.classification.intent`

Field	Type	Required	Description
`type`	string	Yes	High-level intent type such as `informational`, `commercial`, `transactional`, or `ambiguous`
`subtype`	string	No	Interaction pattern such as `comparison`, `product_discovery`, or `signup`
`decision_phase`	string	Yes	Funnel stage such as `awareness`, `consideration`, `decision`, or `action`
`confidence`	number	No	Model certainty from `0.0` to `1.0`
`commercial_score`	number	No	Commercial relevance score from `0.0` to `1.0`
`summary`	string	No	Optional non-normative human-readable explanation; systems must not depend on it for decisioning

`model_output.fallback`

Optional fallback guidance for low-confidence or ambiguous cases.

Field	Type	Required	Description
`applied`	boolean	Yes	Whether fallback logic was invoked
`fallback_intent_type`	string	Yes	Safe fallback intent type
`fallback_monetization_eligibility`	string	Yes	Safe fallback monetization posture
`reason`	string	Yes	Why fallback was used, such as `confidence_below_threshold` or `insufficient_context`

`system_decision`

system_decision contains the auditable operator decision produced after applying thresholds, policy logic, and runtime controls.

`system_decision.policy`

Field	Type	Required	Description
`monetization_eligibility`	string	Yes	Final eligibility state: `allowed`, `allowed_with_caution`, `restricted`, or `not_allowed`
`eligibility_reason`	string	No	Human-readable explanation of the decision boundary that was applied
`decision_basis`	string	No	Decision path such as `score_threshold`, `policy_override`, or a fallback path
`applied_thresholds`	object	No	Concrete numeric thresholds used by the system
`sensitivity`	string	No	Safety sensitivity classification
`regulated_vertical`	boolean	No	Indicates whether regulated-vertical controls apply

`system_decision.opportunity`

Field	Type	Required	Description
`type`	string	Yes	Opportunity type such as `none`, `soft_recommendation`, `comparison_slot`, or `transaction_trigger`
`strength`	string	Yes	Monetization-moment strength: `low`, `medium`, or `high`

`system_decision.intent_trajectory`

Optional ordered list of decision phases across turns, such as:

["research", "consideration", "decision"]

Example

{
  "model_output": {
    "classification": {
      "iab_content": {
        "taxonomy_version": "3.1",
        "tier1": "Business and Finance",
        "tier2": "Business",
        "tier3": "Business I.T."
      },
      "intent": {
        "type": "commercial",
        "subtype": "comparison",
        "decision_phase": "decision",
        "confidence": 0.87,
        "commercial_score": 0.92,
        "summary": "User is evaluating CRM tools for a small team."
      },
      "context": {
        "entities": ["HubSpot", "Zoho"],
        "constraints": {
          "company_size": "small_team"
        }
      }
    }
  },
  "system_decision": {
    "policy": {
      "monetization_eligibility": "allowed",
      "eligibility_reason": "commercial_score_above_threshold",
      "decision_basis": "score_threshold",
      "applied_thresholds": {
        "commercial_score_min": 0.7,
        "confidence_min": 0.6
      },
      "sensitivity": "low",
      "regulated_vertical": false
    },
    "opportunity": {
      "type": "comparison_slot",
      "strength": "high"
    },
    "intent_trajectory": ["research", "consideration", "decision"]
  }
}

Notes

summary is optional and non-normative
systems should not use summary for ranking, matching, policy, or monetization logic
model inference and system decision are intentionally separated for auditability
the public schema identifier is https://agenticintentprotocol.com/schemas/agentic-intent-taxonomy.schema.json
the canonical IAB mapping reference is the IAB Content Taxonomy 3.1 TSV: https://github.com/InteractiveAdvertisingBureau/Taxonomies/blob/develop/Content%20Taxonomies/Content%20Taxonomy%203.1.tsv

Schema file

Full JSON Schema (Draft 2020-12): agentic-intent-taxonomy.schema.json

Welcome

Get Started

AI Platforms

Publisher Access Governance (PAG)

Common

Purpose

Top-Level Structure

`model_output`

`model_output.classification`

`model_output.classification.intent`

`model_output.fallback`

`system_decision`

`system_decision.policy`

`system_decision.opportunity`

`system_decision.intent_trajectory`

Example

Notes

Schema file

​Purpose

​Top-Level Structure

​model_output

​model_output.classification

​model_output.classification.intent

​model_output.fallback

​system_decision

​system_decision.policy

​system_decision.opportunity

​system_decision.intent_trajectory

​Example

​Notes

​Schema file

Purpose

Top-Level Structure

`model_output`

`model_output.classification`

`model_output.classification.intent`

`model_output.fallback`

`system_decision`

`system_decision.policy`

`system_decision.opportunity`

`system_decision.intent_trajectory`

Example

Notes

Schema file