Documentation Index
Fetch the complete documentation index at: https://agenticintentprotocol.com/llms.txt
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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:
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
Schema file
Full JSON Schema (Draft 2020-12): agentic-intent-taxonomy.schema.json
