AGENTS.md Template for Feature Flag Architecture

Overview

AGENTS.md template for Feature Flag Architecture provides a concrete, copyable operating manual designed for AI coding agents that govern flag definitions, rollout, and governance. It enables both single-agent operation and multi-agent orchestration across planning, implementation, evaluation, and human review. Direct answer: use this template to establish roles, handoffs, memory, and tool governance for feature flag workflows handled by AI agents.

When to Use This AGENTS.md Template

• Standardize flag lifecycle management in AI-guided deployments.
• Coordinate multiple agents across planning, implementation, evaluation, and rollout.
• Enforce tool governance, secrets handling, and production safety for feature flags.
• Provide a living, versioned reference for developers and operators.

Copyable AGENTS.md Template

# AGENTS.md

Project Role: Feature Flag Studio

Agent Roster and Responsibilities:
- Planner: Defines feature flag strategy, experiment scope, rollout gates, and success criteria.
- Implementer: Applies changes to code, config, and flag service definitions; enforces naming and environment rules.
- Evaluator: Runs tests, validates metrics, and reports flag health; flags when thresholds are not met.
- Rollout Monitor: Observes live traffic, evaluates risk, and triggers automated rollback when needed.
- Reviewer: Performs final approval on flag changes before production deployment.
- Researcher: Gathers data on flag performance, user impact, and rollback outcomes.

Supervisor / Orchestrator Behavior:
- Coordinates planning, implementation, evaluation, and rollout steps; maintains memory and source-of-truth.
- Enforces handoff rules and ensures all actions are auditable with timestamps and actor IDs.

Handoff Rules Between Agents:
- Planner -> Implementer: deliver flag definitions, naming conventions, and rollout plan.
- Implementer -> Evaluator: provide flag changes, test results, and validation hooks.
- Evaluator -> Rollout Monitor: send flag health signals and acceptance criteria.
- Rollout Monitor -> Reviewer: request final go/no-go for production.
- Researcher -> Planner: supply data insights and recommendations for future flags.

Context, Memory, and Source-of-Truth Rules:
- Source of Truth: central definitions store (flags/definitions.yaml) with versioned commits.
- Context: carry current flag state, environment mappings, and customer impact notes across agents.
- Memory: maintain a per-flag context with last applied version, test results, and rollout status.

Tool Access and Permission Rules:
- Agents may read flag definitions, test results, and metrics; only Implementer may write flag configs in the controlled repo and trigger flag service updates; all changes require approval.
- Secrets: store access tokens in a vault; do not log secrets.

Architecture Rules:
- Use a clear separation between application code and flag definitions; flag service is the authoritative source for runtime behavior.
- One flag per feature with explicit environment gating; avoid semantics drift.

File Structure Rules:
- flags/definitions.yaml
- flags/rollouts.yaml
- orchestrator/README.md
- agents/planner/
- agents/implementer/
- agents/evaluator/
- agents/rollout-monitor/
- agents/reviewer/
- agents/researcher/
- tests/unit/
- tests/integration/
- docs/README.md

Data, API, or Integration Rules:
- Read/write access to flag definitions, rollout manifests, and test results must be versioned.
- All API calls must be authenticated; rate limits must be respected.
- Avoid leaking sensitive data in logs or artifacts.

Validation Rules:
- Each flag change must pass unit tests and integration checks in staging; regression tests must cover at least 2 environments.
- Validation contracts must be updated with flag semantics and rollout criteria.

Security Rules:
- Secrets must never be embedded in code or logs; use vaults and short-lived tokens.
- Production changes require double-check by Reviewer and Rollout Monitor before canary deployment.
- Audit logs must capture actor, timestamp, and outcome.

Testing Rules:
- Unit tests for flag evaluation logic.
- Integration tests for flag service and application boundaries.
- Deployment checks including canary and rollback tests.

Deployment Rules:
- Use CI gates; production changes require at least one reviewer approval and successful canary run.

Human Review and Escalation Rules:
- If rollout risk exceeds threshold, escalate to human review and pause automation.

Failure Handling and Rollback Rules:
- On failure, rollback to previous flag version; revert configuration in definitions and rollout manifests; notify stakeholders.
- Ensure rollback preserves telemetry and state consistency.

Things Agents Must Not Do:
- Do not bypass approvals, mutate governance rules, or perform unapproved data movements.
- Do not modify flag semantics without plan and sign-off.
- Do not persist secrets in logs or artifacts.

Recommended Agent Operating Model

Roles and decision boundaries for feature flag workflows:

• Planner: owns strategy, success criteria, and rollout plan; has decision rights on scope changes.
• Implementer: enforces code and config changes; executes feature flag updates with traceable commits.
• Evaluator: validates tests and metrics; proposes gating thresholds and rollback conditions.
• Rollout Monitor: makes real-time traffic decisions; triggers canary steps and rollback when risk signals fire.
• Reviewer: ensures compliance with governance, checks for security and privacy constraints, and approves deployment.
• Researcher: feeds data-driven insights to Planner for future flags and optimization.

Recommended Project Structure

ai-flags/
  flags/
    definitions.yaml
    rollouts.yaml
  orchestrator/
    plan.md
  agents/
    planner/
    implementer/
    evaluator/
    reviewer/
    rollout-monitor/
    researcher/
  tests/
    unit/
    integration/
  docs/
    readme.md

Core Operating Principles

• Single source of truth for flag definitions; avoid divergence.
• Explicit handoffs with context payloads and trace IDs.
• Automate but require human review for production risk changes.
• Keep security and data handling strictly scoped to the flag lifecycle.
• Auditability and reproducibility of flag decisions and rollouts.

Agent Handoff and Collaboration Rules

• Planner provides clear, versioned flag plans and rollout criteria to Implementer.
• Implementer validates integration with codebase and flag service; passes results to Evaluator.
• Evaluator returns pass/fail with concrete metrics; if failing, loop back to Planner for adjustments.
• Rollout Monitor enforces runbooks and halts on anomalies; escalates to Reviewer when threshold breached.
• Researcher updates Planner with post-incident learnings and performance signals.

Tool Governance and Permission Rules

• Flag definitions writable only by Implementer under Planner-approved plan; other agents read-only or trigger-based actions.
• All API calls authenticated; secrets stored in vault; no secrets in logs.
• Production changes require canary validation and Reviewer approval.
• Access logs must be maintained for all flag updates and rollouts.

Code Construction Rules

• Follow a consistent flag naming convention and environment scoping.
• Code changes and flag updates must be peer-reviewed and linked to the AGENTS.md context.
• Flag semantics must be explicit; avoid implicit behavior changes.

Security and Production Rules

• Use short-lived credentials; rotate tokens after deployments.
• Enforce least privilege for each agent role.
• Detect anomalous flag activity and require escalation.

Testing Checklist

• Unit tests for flag evaluation logic.
• Integration tests between flag service and application surfaces.
• Canary and blue/green rollout validations; rollback tests.
• End-to-end tests documenting flag lifecycle scenarios.

Common Mistakes to Avoid

• Skipping reviews or rushing production without validation.
• Not versioning flag definitions or breaking changes without plan.
• Hard-coding secrets or embedding credentials in logs.
• Ignoring governance and security constraints in automation.

Related implementation resources: AI Agent Use Case for Wholesalers Using Multi-Currency Ledger Trackers To Calculate Foreign Exchange Risk Exposure Across Global Accounts and AI Use Case for Sales Pipeline Reviews and Deal Risk Scoring.

FAQ

What is the purpose of this AGENTS.md Template for feature flag architecture?

It provides a copyable, living operating manual for AI coding agents to govern flag definitions, rollout, evaluation, and governance in a multi-agent setting.

Can this template support multi-agent orchestration?

Yes. It defines roles, handoffs, and governance across planner, implementer, evaluator, reviewer, rollout monitor, and researcher to enable coordinated flag workflows.

Where should I store and version this AGENTS.md Template?

In the project repository root as a living document; version it with the feature flag manifests and related code.

What about security and data handling in this template?

It mandates vault-stored secrets, audited access, and production-change gates; no secrets written to logs or code.

How do I customize this for my feature flags?

Replace generic examples with real flag names, environments, service boundaries, and measurable rollout criteria; extend the validation rules for your stack.