AGENTS.md Template: Application Level Caching

Overview

Direct answer: This AGENTS.md Template provides a complete operating manual for AI coding agents implementing application level caching, enabling single-agent and multi-agent orchestration with clear handoffs, key naming, and governance.

The template governs the caching workflow, including TTL policies, cache keys, invalidation hooks, memory rules, and cross-agent decision boundaries, so teams can run caching improvements with auditable governance.

When to Use This AGENTS.md Template

• When building an application level caching layer where AI agents design, implement, test, and orchestrate cache behavior
• When multi-agent orchestration and handoffs are required to ensure correctness and safety
• When you need a repeatable, auditable operating model for cache TTLs, key naming, and invalidation hooks
• When you require governance over tool usage, security, and production deployment of cache changes

Copyable AGENTS.md Template

# AGENTS.md

Project role: Cache Architect for App Level Caching
Agent roster and responsibilities:
- Planner: Defines caching strategy, TTL policy, and key naming
- Implementer: Translates strategy into code, configs, and agent actions
- Reviewer: Validates design, memory context, and outputs
- Tester: Executes tests for cache hit/miss, eviction, and invalidation
- Researcher: Gathers policy docs, best practices, and threat models
- Domain Specialist: Provides domain-specific key schemes and invalidation triggers
Supervisor or orchestrator behavior:
- Orchestrator coordinates tasks, enforces sequence, and mediates conflicts
- Ensures idempotent operations and traces actions to the source of truth
Handoff rules between agents:
- Planner → Implementer when strategy is ready
- Implementer → Reviewer after generating artifacts
- Reviewer → Tester for validation
- Researcher/Domain Specialist can provide inputs at any stage with approval
Context, memory, and source-of-truth rules:
- Maintain a single source of truth: caching configs, TTLs, and key naming in repo and in memory context
- Memory scope per run includes: current TTL, key prefix, cache backend details, invalidation events
- All actions must be sourced from documented policies and code
Tool access and permission rules:
- Implementations may read and write code, configs, and tests in repo
- Use secrets manager for keys and credentials; never hard-code
- Access to cache backends (redis/memcached) through approved services only
Architecture rules:
- Caching layer must be isolated from core business logic
- TTLs must be explicit and overridable via config
- Invalidation should occur on explicit events and/or time-based TTL expiry
File structure rules:
- Keep all cache schemas, key templates, and TTL policies under cache/ in the repo
Data, API, or integration rules when relevant:
- Keys must be namespaced by service and feature domain
- Invalidate on data model changes and deployment hooks
Validation rules:
- Validate cache hits/misses with deterministic datasets
- Ensure eviction policies match TTL expectations
Security rules:
- Secrets must be stored securely and rotated periodically
- Do not expose real cache keys in logs or traces
Testing rules:
- Unit tests for key generation and TTL calculation
- Integration tests for end-to-end cache reads/writes
Deployment rules:
- Changes to cache layer require a canary test and rollback plan
Human review and escalation rules:
- Escalate to platform owner if policy deviations are detected
Failure handling and rollback rules:
- If cache policy fails, rollback to previous TTL and clear newly written keys
Things Agents must not do:
- Do not bypass security checks
- Do not write to production databases from cache layer
- Do not circumvent the orchestrator
- Do not operate outside the defined TTL policy

Recommended Agent Operating Model

The operating model assigns clear roles with decision boundaries and escalation paths to ensure safe, auditable caching behavior. Agents collaborate to design, implement, validate, and monitor the application level caching strategy, while preventing context drift and architecture drift.

Recommended Project Structure

project-root/
├── apps/
│   ├── web-app/
│   │   └── src/
│   └── api-service/
│       └── src/
├── caching/
│   ├── configs/
│   │   ├── ttl-config.yaml
│   │   └── cache-keys.json
│   ├── policies/
│   │   └── invalidation.md
│   └── layers/
│       ├── in-memory/
│       └── redis/
├── agents/
│   ├── planner/
│   ├── implementer/
│   ├── reviewer/
│   ├── tester/
│   └── researcher/
├── docs/
│   └── agents-md-templates/
├── deployments/
│   └── canaries/

Core Operating Principles

• Clarity of responsibility and auditability in every agent action
• Idempotent operations and deterministic outcomes
• Strict memory and source-of-truth discipline
• Explicit cache TTL and key naming policies
• Secure handling of secrets and production changes

Agent Handoff and Collaboration Rules

Define when planners hand off to implementers, how reviewers validate outputs, and how testers verify cache behavior. Domain specialists provide domain-aligned key schemas and invalidation triggers. Escalation paths must be explicit for policy deviations or failures.

Tool Governance and Permission Rules

• Only approved tools and services may be used for cache config, key generation, and deployment
• Secrets must be retrieved from secrets managers; no plaintext keys in logs or code
• All API calls to production caches require approval gates and canary testing
• Production changes must pass validation, review, and rollback plans

Code Construction Rules

• Strings and keys must be namespaced and deterministic
• TTL values must be sourced from JSON/YAML configs, not hard-coded
• Cache invalidation hooks must be triggered by data changes and events
• Code must be tested with clear unit and integration tests for caching

Security and Production Rules

• Encrypt secrets at rest and in transit
• Audit cache access and invalidate when required by policy
• Do not bypass admission controls for production cache updates

Testing Checklist

• Unit tests for key generation and TTL logic
• Integration tests for cache read/write paths
• Canary tests for policy changes in staging
• Rollout and rollback validation in production-like environments

Common Mistakes to Avoid

• Ignoring TTL drift and stale reads
• Bypassing orchestrator or security checks
• Untracked policy changes without proper validation

Related implementation resources: AI Use Case for Visa Consultants Using Government Portals To Check Application Documents for Missing Requirements and AI Use Case for Corporate Event Managers Using Slack To Orchestrate Day-Of Venue Tasks Across Multi-Department Teams.

FAQ

What is this AGENTS.md Template for?

This AGENTS.md Template defines the operating context for AI coding agents to implement application level caching, including how they coordinate, what they can access, and how decisions are audited.

How does multi-agent orchestration work for application level caching?

Planner designs strategy, Implementer builds artifacts, Reviewer validates, Tester verifies behavior, and Domain Specialist provides domain-aligned keys; the orchestrator coordinates handoffs and enforces policy gates.

How are cache invalidations handled in this template?

Invalidation is triggered by data model changes, deployment hooks, and TTL expiry events, and must be logged to the source of truth with a reversible rollback path.

What about secrets and production safety?

Secrets are retrieved from a secrets manager, never hard-coded, and all production changes require canaries, validation, and rollback plans.

What should I paste into the AGENTS.md Block?

The block starting with # AGENTS.md contains the project role, agent roster, supervisor behavior, handoff rules, context rules, tool access, architecture rules, file structure, data rules, validation, security, testing, deployment, human review, failure handling, and things agents must not do.