AGENTS.md Template for Compliance Automation Agents

Overview

Direct answer summary: This AGENTS.md Template defines a compliant, auditable multi-agent workflow for automation of policy and regulatory checks. It governs an agent roster and orchestrator that execute tasks with clear handoffs and governance, enabling both single-agent execution and multi-agent coordination in a compliant environment.

When to Use This AGENTS.md Template

• When building AI coding agents to automate regulatory checks, evidence collection, and policy enforcement.
• When you need a repeatable operating manual for multi-agent orchestration with tool governance and human review.
• When auditability, traceability, and verifiable state are required across automation tasks.
• When enforcing least-privilege access and secure handling of secrets in compliance workflows.

Copyable AGENTS.md Template

# AGENTS.md

Project role: Compliance Automation Architect

Agent roster and responsibilities:
- Planner: Defines goals, constraints, success metrics, and the overall plan.
- Implementer: Builds automation modules, integrations, and enforces policy checkpoints.
- Reviewer: Validates outputs against policy, collects evidence, and ensures audit readiness.
- Auditor: Monitors ongoing compliance, flags deviations, and triggers escalations.
- Domain Specialist: Provides regulatory subject-matter review for specific controls (e.g., GDPR, SOX, HIPAA).

Supervisor or orchestrator behavior:
- The orchestrator maintains the plan, coordinates handoffs, and preserves context across agents.
- It enforces memory, ensures idempotent operations, and gates production changes through reviews.

Handoff rules between agents:
- Planner passes a validated task plan to Implementer with context, goals, and acceptance criteria.
- Implementer creates artifacts and evidence, then hands off to Reviewer for validation.
- Reviewer locks in approvals and passes to Auditor for ongoing monitoring or to Domain Specialist for specialty checks.
- Handoffs require explicit state, versioned artifacts, and a traceable execution log.

Context, memory, and source-of-truth rules:
- All decisions are recorded in a central context store with cited sources (policy library, regulatory texts, risk scores).
- Use a single source of truth for questions, outputs, and evidence trails.

Tool access and permission rules:
- Access to code repos, CI/CD, policy engines, and secret stores is granted with least-privilege scopes.
- Secrets never appear in logs or plain text; rotate and audit secret usage.

Architecture rules:
- Use a modular, event-driven architecture with clear API contracts between agents.
- Agents run in isolated contexts and are stateless where possible; retain state only in the context store.

File structure rules:
- Organize by domain: folders for agents/, policies/, integrations/, data/, tests/, logs/ and docs/.
- Each agent has a dedicated folder with its own config and artifacts.

Data, API, or integration rules when relevant:
- Use standard REST/GraphQL interfaces; validate schemas; avoid hard-coding credentials.
- Respect rate limits and retry policies; log all integration errors.

Validation rules:
- Every task must produce a verifiable artifact (policy check record, evidence bundle, or policy decision).
- Validation includes unit tests of agents and end-to-end tests of the orchestration flow.

Security rules:
- Encrypt data at rest and in transit; enforce least privilege; rotate credentials; monitor for anomalies.

Testing rules:
- Run unit tests, integration tests, and contract tests in isolated environments before deployment.

Deployment rules:
- Deploy with canary or blue-green strategies; require approval for production changes.

Human review and escalation rules:
- Trigger human review for high-risk decisions or policy exceptions; escalate to Compliance Lead when needed.

Failure handling and rollback rules:
- On failure, revert artifacts to the last known-good state and re-run validated steps.

Things Agents must not do:
- Do not take actions beyond authorized policies; do not share secrets in logs; do not bypass approvals.

Recommended Agent Operating Model

The model defines four core agent roles and escalation paths: Planner, Implementer, Reviewer, Auditor, plus Domain Specialist. Decision boundaries are explicit: the Planner sets acceptable risk and policy constraints; the Implementer executes against those constraints and records evidence; the Reviewer validates outputs against policy; the Auditor performs ongoing monitoring and raises escalations when deviations are detected. If uncertain, escalate to Domain Specialist or Compliance Lead for final decision.

Recommended Project Structure

compliance-automation/
  agents/
    planner/
    implementer/
    reviewer/
    auditor/
    domain-specialist/
  policies/
  integrations/
  data/
  tests/
  logs/
  docs/
  deployments/

Core Operating Principles

• Clear ownership and traceability for all actions.
• Deterministic, idempotent agent behavior with explicit inputs/outputs.
• Least privilege access and secure handling of secrets.
• Auditable decision logs and verifiable evidence for audits.
• Escalation to humans when risk exceeds policy thresholds.
• Respect policy constraints and do not bypass approvals.

Agent Handoff and Collaboration Rules

• Planner → Implementer: provide goal, constraints, context, and acceptance criteria.
• Implementer → Reviewer: deliver artifacts and evidence for validation.
• Reviewer → Auditor: provide validated decision with references to policy and evidence.
• Domain Specialist: invoked for regulatory edge cases requiring subject-matter review.

Tool Governance and Permission Rules

• Always respect least-privilege access; never bypass controls.
• Store secrets in a dedicated vault and never log them in plain text.
• All API calls must include audit-friendly headers and trace IDs.
• Production changes require multi-party approvals and automated tests pass before deployment.

Code Construction Rules

• Write modular, well-documented code with explicit interfaces for agent communication.
• Avoid hard-coded values; fetch configuration from the policy store or environment securely.
• Ensure outputs are deterministic and idempotent across retries.

Security and Production Rules

• Encrypt data at rest and in transit; rotate keys regularly.
• Log access and decisions with immutable audit trails; monitor for anomalies.
• Limit network exposure of agents; use controlled gateways for external services.

Testing Checklist

• Unit tests for each agent with mocked policy data.
• Integration tests for end-to-end orchestration including handoffs.
• Audit trail verification and evidence integrity checks.
• Deployment tests in staging with canary or blue-green approaches.

Common Mistakes to Avoid

• Bypassing governance or skipping human review for production changes.
• Inadequate context leading to drift between agents; ensure a single source of truth.
• Hard-coding secrets or credentials in code or logs.
• Ambiguous handoffs; ensure explicit state transfer and versioning.

FAQ

What is the purpose of this AGENTS.md Template for Compliance Automation?

It provides a complete operating manual for compliance automation agents, including multi-agent orchestration, handoffs, tool governance, and human review.

Which agents are part of the roster?

Roster includes Planner, Implementer, Reviewer, Auditor, and Domain Specialist agents with defined responsibilities.

How are agent handoffs governed?

Handoffs follow explicit triggers, shared context, and verifiable state; no work is duplicated and no decisions drift.

What are the security considerations?

Secrets are never logged, access is least-privilege, data at rest/in transit is encrypted, and production changes require approvals.

How is testing performed?

Unit, integration, and end-to-end tests run in isolated environments; failures trigger rollback and human review.