Core Feature

Policy Gates turn governance rules into enforceable AI control points

Policy Gates evaluate governed AI events against organization-defined policy conditions. Each gate produces an explicit outcome: Approve, Deny, Escalate, or Inspect. The result is stored as a tamper-evident receipt so the organization can prove what decision was made, why it was made, and which policy version was active at the time.

Gate Outcome Model

Approve

The event satisfies the required policy conditions. Action proceeds automatically.

Deny

The event violates policy and is blocked. No further action permitted.

Escalate

The event requires human review or higher authority before proceeding.

Inspect

The event is allowed to continue but flagged for additional review or monitoring.

Policy Definition Example

Policies are defined as JSON documents that specify conditions, thresholds, and failure actions.

Credit Model Deployment Policy

Requires validation, approval, and lineage before model deployment

{
  "policy_id": "credit-model-deployment-policy",
  "policy_version": "1.0.0",
  "gate": "model_deployment_gate",
  "description": "Require validation, approval, and lineage before deployment",
  "conditions": [
    {
      "field": "validation.accuracy",
      "operator": ">=",
      "value": 0.92,
      "on_fail": "deny",
      "reason_code": "ACCURACY_BELOW_THRESHOLD"
    },
    {
      "field": "validation.bias_review_completed",
      "operator": "==",
      "value": true,
      "on_fail": "escalate",
      "reason_code": "BIAS_REVIEW_REQUIRED"
    },
    {
      "field": "lineage.dataset_hash_present",
      "operator": "==",
      "value": true,
      "on_fail": "deny",
      "reason_code": "MISSING_DATASET_LINEAGE"
    }
  ],
  "default_outcome": "deny"
}

Gate Evaluation Result

When an event is evaluated, AGEI produces a structured outcome with policy context, reason codes, and timestamp.

Evaluation Receipt

Model deployment request escalated for bias review

{
  "event_type": "model_deployment_requested",
  "model_id": "credit-risk-v3",
  "policy_id": "credit-model-deployment-policy",
  "gate": "model_deployment_gate",
  "outcome": "escalate",
  "reason_code": "BIAS_REVIEW_REQUIRED",
  "policy_version": "1.0.0",
  "evaluated_at": "2026-05-04T15:20:00Z",
  "receipt_id": "rcpt_gate_001",
  "content_hash": "sha256:a3f2e1..."
}

Policy Gate Workflow

1. AI Lifecycle Event

Model deployment, agent action, or governed workflow triggers gate evaluation

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2. Metadata Collected

Event payload, identifiers, and context gathered for policy evaluation

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3. Policy Gate Evaluates Conditions

Each condition in the policy is checked against event data

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4. Outcome Determined

Approve / Deny / Escalate / Inspect based on condition results

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5. Receipt Written to Evidence Vault

Tamper-evident record of the decision with policy version, reason codes, and hash

Why Policy Gates Matter

Enforcement, Not Just Monitoring

Policy gates are not dashboards. They are control points that block, escalate, or flag governed events based on organizational rules.

Verifiable Evidence

Every gate evaluation creates a tamper-evident receipt showing the policy version, evaluation timestamp, and reason for the outcome.

Policy Versioning

Gates reference immutable policy versions. Organizations can prove which policy was active when a decision was made.

Separation of Concerns

Policy gates evaluate technical conditions. Human reviewers make governance decisions. Evidence receipts capture both.

AGEI does not treat policy gates as informal checkpoints. It treats them as governed lifecycle events with evidence, authority, timing, policy context, and cryptographic traceability.

Ready to Implement Policy Gates?

Read the technical documentation or explore a working example.