AGENTS.md TemplatesAGENTS.md Template

Caching Architecture Design AGENTS.md Template

AGENTS.md Template for caching architecture design guiding AI coding agents in multi-agent orchestration.

AGENTS.md TemplateCachingAI coding agentsmulti-agent orchestrationagent handoff rulestool governancecache designcache invalidationsecurity rulesworkflow orchestrationCaching Architecture Template

Target User

Developers, architects, and engineering leaders implementing caching strategies with AI agents

Use Cases

  • Design robust caching layers with AI agents
  • Coordinate multi-agent caching for writes and invalidations
  • Define handoffs between planner, cache agent, and reviewer
  • Govern cache API usage and secrets in production

Markdown Template

Caching Architecture Design AGENTS.md Template

# AGENTS.md
Project role: Cache Architect for AI agents
Agent roster and responsibilities:
- Planner
- CacheAgent
- InvalidationAgent
- Reviewer
- Orchestrator
Supervisor or orchestrator: Orchestrator
Handoff rules:
- Planner → CacheAgent: pass cache policy and initial config
- CacheAgent → InvalidationAgent: propagate cache policy and invalidation strategy
- InvalidationAgent → Reviewer: provide test results and modified policies
- Reviewer → Orchestrator: get approval to push changes
Context memory and source of truth:
- All decisions and code come from the central repository and design docs
- Use a single source of truth for cache keys, TTLs, and invalidation rules
Tool access and permission rules:
- Agents may call caching APIs under restricted scope
- Secrets must be retrieved from a secure vault and never hard coded
Architecture rules:
- Stateless planner decisions, idempotent cache updates
- Central orchestrator enforces order and guards against race conditions
File structure rules:
- All artifacts live under project-root directory with clear naming
Data API or integration rules:
- Cache API calls must be authenticated and rate limited
- Invalidation events propagate through orchestrator
Validation rules:
- All new cache policies validated with unit tests and integration tests
Security rules:
- No direct production keys in agent prompts
- All communications encrypted at rest and in transit
Testing rules:
- Run cache eviction and warmup tests
- Simulate failing nodes and recovery
Deployment rules:
- Deploy only after approval and successful tests
Human review and escalation rules:
- If tests fail or policy conflict occurs, escalate to human reviewer
Failure handling and rollback rules:
- Roll back cache policy changes if invalidation misses occur
Things Agents must not do:
- Do not bypass orchestrator or security controls
- Do not edit production cache keys directly

Overview

Direct answer style summary: This AGENTS.md template defines the caching architecture agent workflow including rosters, memory, handoffs, tool governance, and security rules.

When to Use This AGENTS.md Template

  • When building or maintaining a caching layer backed by AI coding agents
  • When coordinating multi-agent orchestration across planner, cache agent, invalidation agent, and reviewer
  • When you need a single source of truth for architecture, memory, and handoff rules

Copyable AGENTS.md Template

# AGENTS.md
Project role: Cache Architect for AI agents
Agent roster and responsibilities:
- Planner
- CacheAgent
- InvalidationAgent
- Reviewer
- Orchestrator
Supervisor or orchestrator: Orchestrator
Handoff rules:
- Planner → CacheAgent: pass cache policy and initial config
- CacheAgent → InvalidationAgent: propagate cache policy and invalidation strategy
- InvalidationAgent → Reviewer: provide test results and modified policies
- Reviewer → Orchestrator: get approval to push changes
Context memory and source of truth:
- All decisions and code come from the central repository and design docs
- Use a single source of truth for cache keys, TTLs, and invalidation rules
Tool access and permission rules:
- Agents may call caching APIs under restricted scope
- Secrets must be retrieved from a secure vault and never hard coded
Architecture rules:
- Stateless planner decisions, idempotent cache updates
- Central orchestrator enforces order and guards against race conditions
File structure rules:
- All artifacts live under project-root directory with clear naming
Data API or integration rules:
- Cache API calls must be authenticated and rate limited
- Invalidation events propagate through orchestrator
Validation rules:
- All new cache policies validated with unit tests and integration tests
Security rules:
- No direct production keys in agent prompts
- All communications encrypted at rest and in transit
Testing rules:
- Run cache eviction and warmup tests
- Simulate failing nodes and recovery
Deployment rules:
- Deploy only after approval and successful tests
Human review and escalation rules:
- If tests fail or policy conflict occurs, escalate to human reviewer
Failure handling and rollback rules:
- Roll back cache policy changes if invalidation misses occur
Things Agents must not do:
- Do not bypass orchestrator or security controls
- Do not edit production cache keys directly

Recommended Agent Operating Model

The agent roster above defines clear roles with bounded decision spaces. The Orchestrator coordinates handoffs and enforces constraints. Escalation paths are defined for test failures or policy conflicts.

Recommended Project Structure

project-root/
  agents/
    planner/
    cacheAgent/
    invalidationAgent/
    reviewer/
  orchestrator/
  caches/
    policies/
    keys/
  data/
  tests/
  docs/
  infra/

Core Operating Principles

  • Single source of truth for cache policy and key space
  • Clear handoffs with explicit inputs and outputs
  • Guardrails enforced by the orchestrator
  • Agent permissions scoped to least privilege
  • Auditability and traceability for all changes

Agent Handoff and Collaboration Rules

  • Planner communicates policy decisions to CacheAgent with goals and constraints
  • CacheAgent performs caching operations and reports results to InvalidationAgent
  • InvalidationAgent triggers cache invalidations and notifies Reviewer
  • Reviewer validates changes before Orchestrator approves deployment

Tool Governance and Permission Rules

  • All tool calls must be auditable and tied to a ticket
  • Secrets are accessed only via vault and are rotated periodically
  • Production systems require enforced approvals and change control

Code Construction Rules

  • Idempotent cache updates
  • Deterministic TTL and invalidation behavior
  • Logging of decisions and results for replayability

Security and Production Rules

  • Encrypt sensitive data at rest and in transit
  • Limit exposure of feature flags and keys to the minimum scope
  • Monitor for anomalous cache miss patterns and rollback if needed

Testing Checklist

  • Unit tests for each agent
  • Integration tests for end-to-end caching workflow
  • Smoke tests in staging before production deploy

Common Mistakes to Avoid

  • Skipping explicit handoffs and relying on implicit context
  • Bypassing the orchestrator or security controls
  • Overloading a single agent with cross-cutting responsibilities

Related implementation resources: AI Use Case for Content Marketers Using Wordpress To Auto-Translate Blog Posts Into Multiple Languages and AI Use Case for Sales Pipeline Reviews and Deal Risk Scoring.

FAQ

What is the purpose of this AGENTS.md Template for caching architecture design?

It defines a repeatable, audit friendly operating model for AI coding agents to design and operate a caching layer with clear roles, handoffs, and governance.

How does multi-agent orchestration work for caching?

Agents coordinate via a centralized orchestrator, each with a defined role such as planner, cache agent, and reviewer, exchanging context and results through well scoped handoffs.

Who is responsible for handoffs and approvals?

The orchestrator enforces handoffs between agents and ensures reviewer approvals before code or configuration changes move to production.

What security considerations apply to cache keys and secrets?

Cache keys must be scoped with least privilege, secrets accessed only through secure vaults, and all tool calls audited with role based access controls.

How do you validate the caching workflow before deployment?

Run unit and integration tests for cache behavior, simulate traffic, verify invalidation correctness, and require human review for production rollouts.