AGENTS.md Template for API Response Caching Strategy

Overview

The AGENTS.md Template for API Response Caching Strategy defines how AI coding agents collaborate to implement, monitor, and evolve a robust API response caching policy. It governs a single agent and multi-agent orchestration pattern, with clear boundaries, handoffs, and a centralized memory of policy and decisions. This AGENTS.md Template aligns team practice with tool governance, human review, and auditable decision trails, ensuring reliable, cache-consistent responses across services.

Direct answer: This AGENTS.md Template provides an operating manual for an API response caching workflow, enabling coordinated planning, implementation, validation, and rollback across agents with an orchestrator at the center.

When to Use This AGENTS.md Template

• You are implementing a cross-service API response caching layer with TTL, stale-while-revalidate, and invalidation policies.
• You need clear handoff rules between planner, implementer, tester, reviewer, researcher, and domain specialist agents.
• You require auditable tool access, secrets handling, and production-safe deployment gates.
• You want a repeatable, documented operating model that scales from a single agent to multi-agent orchestration.

Copyable AGENTS.md Template

# AGENTS.md
Project: API Response Caching Strategy

Role: API Caching Project Lead and Orchestrator

Agent roster and responsibilities
- Planner: designs caching policy, cache keys, TTLs, eviction rules, and invalidation triggers
- Implementer: builds cache storage interactions, integrates with backend services, and implements policies
- Reviewer: audits policy correctness, security controls, and risk surfaces
- Tester: creates and runs unit/integration tests for cache behavior and performance
- Researcher: investigates edge cases, stale data scenarios, and performance gaps
- Domain Specialist: ensures domain-specific caching semantics (e.g., cache warmth for auth tokens, user-context keys)

Supervisor or orchestrator behavior
- Orchestrator coordinates all agents, enforces policy, and records decisions in a central policy store
- All outputs are versioned and auditable; decisions require supervisor approval for production changes

Handoff rules between agents
- Planner -> Implementer: policy and keys documented in policy store; handoff triggers cache key creation and TTLs
- Implementer -> Tester: deploys cache changes to test environment and runs tests
- Tester -> Reviewer: presents test results and risk assessment
- Reviewer -> Orchestrator: approves changes or requests adjustments; orchestrator records outcome
- Domain Specialist involvement as needed for domain-specific decisions

Context, memory, and source-of-truth rules
- Central policy repository stores cache policies, TTLs, and eviction rules
- All agents read from and write to the policy store; memory is session-scoped and refreshed from the source of truth
- Source of truth is the canonical cache policy document with change history

Tool access and permission rules
- Tools: HTTP client, cache service API, backend service proxies, secret store, CI/CD gates
- Secrets never hard-coded; access granted through scoped permissions and short-lived tokens
- Any production run requires orchestrator approval and a canary deployment gate

Architecture rules
- Microservice boundary alignment; no cross-service state leakage
- TTLs and invalidation signals enforce consistency across services
- Cache warming and prefetch strategies to avoid cold starts

File structure rules
- policies/
- src/
- tests/
- docs/
- orchestrator/
- config/

Data, API, or integration rules
- Use REST/HTTP cache headers with explicit cache-control policies
- Invalidation events trigger immediate policy updates and refreshes across services

Validation rules
- Unit tests for cache key generation and TTL calculations
- Integration tests for cross-service invalidation scenarios
- End-to-end tests simulating cache misses and warmups

Security rules
- Never cache sensitive data without encryption and strict access controls
- Rotate API keys and ensure secret rotation is automatic

Testing rules
- Regression tests for policy changes; performance tests for TTL impact
- Canary tests for production deployments

Deployment rules
- Deploy policy changes via orchestrator with canary rollout
- Rollback to previous policy if tests fail in canary

Human review and escalation rules
- Any change to cache invalidation logic requires human review
- If a failure occurs in production, escalate to on-call engineer and rollback

Failure handling and rollback rules
- If cache policy causes incorrect responses, rollback to previous policy and invalidate affected keys
- Maintain a changelog of policy changes and rollback events

Things Agents must not do
- Bypass the central policy store
- Expose secrets in logs or messages
- Modify global cache policy without orchestration approval
- Create non-deterministic cache keys or inconsistent TTLs

Recommended Agent Operating Model

Roles are defined to balance autonomy with governance. Planner designs policy and handoff points. Implementer executes according to policy. Tester validates behavior. Reviewer checks correctness and risk. Researcher and Domain Specialist provide domain-specific insights. The Orchestrator coordinates all agents, enforces gates, and maintains a single source of truth for policy decisions. Escalation paths route issues to humans when needed, with traceable decisions in the policy store.

Recommended Project Structure

api-cache-workflow/
├── policies/            # Central policy store with TTLs, eviction, and invalidation rules
├── src/                 # Implementation code for cache integration and service adapters
├── tests/               # Unit and integration tests for caching behavior
├── docs/                # Documentation and runbooks
├── orchestrator/          # Orchestrator logic and state machine
├── config/              # Environment-specific configuration
├── tools/               # Utility scripts for validation and deployment

Core Operating Principles

Agent Handoff and Collaboration Rules

Tool Governance and Permission Rules

Code Construction Rules

Security and Production Rules

Testing Checklist

• Performance tests to measure cache hit rate and latency impact.
• Canary tests before production rollout of policy changes.

Common Mistakes to Avoid

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FAQ

What is this AGENTS.md Template for API Response Caching Strategy?

This AGENTS.md Template defines the operating manual for coordinating AI coding agents to implement and govern an API response caching strategy, including multi-agent orchestration and handoffs.

How do agents handoff cache policy changes safely?