KYA Regulated Agent Human Gate

Treat the language model as a planner and evidence collector, never as the final authorization boundary. Put deterministic policy evaluation and receipt verification in trusted code outside the model.

Enforce These Invariants

1. Freeze the exact action before requesting approval.
2. Hash the canonical action and bind every decision and approval to that hash.
3. Accumulate controls from every matched rule. A stronger routing decision must not erase another required control.
4. Keep authentication, identity, authority, business approval, and compliance review separate.
5. Let only a trusted policy service issue a short-lived, signed, one-time delegation receipt.
6. Verify the receipt against the final action immediately before execution.
7. Consume the receipt nonce and execute idempotently. Reject replay, expiry, mutation, tenant mismatch, policy mismatch, and signer mismatch.
8. Fail closed when screening, evidence, authorization, audit writing, or provider state is uncertain.
9. Never let an AI agent approve its own action or mark a human control as satisfied.

Build The Gate

1. Normalize the action with templates/action-intent-schema.json.
   • Represent money as integer value_minor, never a floating-point amount.
   • Use stable tenant, subject, agent, target, and idempotency identifiers.
   • Store references or fingerprints, not raw credentials, biometrics, or identity documents.
2. Freeze and hash the action with scripts/kya_receipt.py hash.
3. Evaluate all policy rules deterministically.
   • Return a routing decision and a separate cumulative required_controls list.
   • Set authorization_status to PENDING_CONTROLS, APPROVED, or DENIED.
   • Treat ALLOW as executable only when every required control is satisfied.
4. Collect control evidence through trusted providers and independent human actors.
5. Re-evaluate when evidence or action data changes. Never patch an old approval onto a new action.
6. Issue a delegation receipt only when the decision validates against templates/gate-decision-schema.json and every required control is satisfied.
7. Verify and atomically consume the receipt at the authorized executor.
8. Append the decision, control, receipt, execution, and failure events using templates/audit-log-schema.json.
9. Run mutation, replay, expiry, fail-closed, and false-allow evaluations before launch.

Resolve Decisions And Controls

Use decision as the current routing state, not as the complete authorization record.

• ALLOW: no unsatisfied control remains; standard audit logging still applies.
• USER_CONFIRMATION: require confirmation of the frozen amount, target, fees, and consequence.
• STEP_UP_AUTHENTICATION: require an OTP, passkey, or equivalent trusted authentication result.
• IDENTITY_VERIFICATION: require current identity and, when relevant, authority evidence.
• HUMAN_APPROVAL: require an independent authorized approver with the complete action summary.
• COMPLIANCE_REVIEW: hold execution until an authorized compliance process resolves uncertainty.
• DENY: stop execution. Ordinary approval cannot override a terminal denial.

Union controls across matching rules. For example, a USD 8,000 payment to a new beneficiary can require all of user_confirmation, identity_verification, beneficiary_verification, and independent_human_approval, even when the routing decision is HUMAN_APPROVAL.

Use The MCP Execution Contract

Implement every tool named in templates/mcp-tool-contract-example.json and preserve this trust sequence:

1. evaluate_financial_action
2. create_control_challenge
3. record_control_result
4. issue_delegation_receipt
5. verify_and_consume_receipt
6. execute_authorized_action
7. write_audit_event

The executor must not accept a decision ID, chat confirmation, screenshot, or approval ID as authorization. It must accept only a verified receipt bound to the final action hash.

Exercise The Reference Guard

Set a test-only signing key and run the examples:

$env:KYA_RECEIPT_SECRET = "replace-with-a-test-secret-at-least-32-bytes"
python scripts/kya_receipt.py hash --action examples/supplier-payment-action.json
python scripts/kya_receipt.py issue --action examples/supplier-payment-action.json --decision examples/supplier-payment-decision.json --output receipt.json
python scripts/kya_receipt.py verify --action examples/supplier-payment-action.json --receipt receipt.json --policy-version 2.0.0 --nonce-db receipt-nonces.db
python -m unittest discover -s scripts -p "test_*.py"

Use HMAC only as the self-contained reference implementation. In production, place signing in a managed KMS or HSM, rotate keys by kid, authenticate MCP callers, and combine nonce consumption with an idempotent execution record.

Produce This Output

## Action Intent And Hash
## Agent Scope And Trust Boundary
## Risk And Matched Rules
## Cumulative Required Controls
## Control Evidence And Separation Of Duties
## Delegation Receipt And MCP Execution Contract
## Audit And Data-Minimization Plan
## Failure And Safe Fallbacks
## Evaluation Cases And Acceptance Metrics
## Open Assumptions

Read Supporting Material

• Read resources/risk-taxonomy.md for risk factors.
• Read resources/human-gate-policy.md for cumulative control and terminal decision rules.
• Read resources/identity-verification-routing.md to separate identity, authentication, authority, and approval.
• Read resources/audit-evidence-checklist.md for retained evidence.
• Read resources/agent-evaluation-guide.md for launch evaluations.
• Read resources/regulated-finance-use-cases.md for scenario patterns.
• Start policy work from templates/policy-template.yaml.

State The Boundary

Do not claim regulatory certification, legal advice, or production-grade immutability. State which controls are implemented, mocked, provider-backed, or still require legal and compliance approval.