AGENTS.md Template: Read-Through Cache Architecture

Overview

Direct answer: This AGENTS.md template governs a read-through cache architecture for AI coding agents and multi-agent orchestration. It provides a complete operating manual that supports both a single agent and coordinated, multi-agent workflows with explicit handoffs, memory rules, and governance. It enables reliable cache-backed reads, deterministic fallbacks to source data, and auditable decision trails across planner, implementer, reviewer, tester, researcher, and domain specialist agents.

When to Use This AGENTS.md Template

When your AI coding agents rely on a read-through cache to satisfy requests with low latency and proper freshness guarantees.
When you need a clear, copyable operating context for single-agent or multi-agent orchestration with explicit handoffs.
When you require tool governance, memory rules, and source-of-truth discipline across cache reads and data sources.
When you must document validation, security, testing, deployment, and escalation procedures in a single template.

Copyable AGENTS.md Template

# AGENTS.md

Project role
- Read-Through Cache Orchestrator for AI coding agents

Agent roster and responsibilities
- Planner: decides whether data should be served from cache or fetched from source
- Implementer: builds the read path, reads and hydrates data from cache or source
- Reviewer: validates correctness of data, cache keys, and TTL policy
- Tester: executes integration tests for cache hits, misses, and latency
- Researcher: investigates caching strategies, invalidation, and TTL tuning
- Domain Specialist: ensures data domain constraints, privacy, and TTL semantics

Supervisor or orchestrator behavior
- The orchestrator coordinates agents, enforces memory rules, ensures sources of truth, and gates tool usage
- All actions are validated against the policy catalog before execution

Handoff rules between agents
- Planner → Implementer on cache miss or TTL invalidation
- Implementer → Reviewer after implementation is complete
- Reviewer → Tester for integration tests and acceptance criteria
- Researcher ↔ Domain Specialist for TTL policy decisions and domain constraints

Context, memory, and source-of-truth rules
- Context includes: request payload, user identity, cache keys, TTL, and data schema
- Memory persists for the duration of the workflow; past runs are reference only
- Source-of-truth is the canonical data source (backend DB or external API)

Tool access and permission rules
- Agents may access read-only caches unless a write permission is granted by policy
- Secrets managed via vault; rotation required for any cache write keys

Architecture rules
- Implement a two-tier cache: in-memory for hot data, distributed cache for shared data
- Cache keys must be stable and versioned with data schema

File structure rules
- src/cache for cache logic and policy
- services/cache for integration points
- tests/cache for tests and mocks

Data, API, or integration rules when relevant
- Cache get, set, and invalidate endpoints; ensure idempotent transforms
- Use optimistic reads where appropriate; fall back to source on miss

Validation rules
- Validate freshness, TTL correctness, and data correctness against source of truth
- Validate that cache writes do not leak sensitive fields

Security rules
- Never log secrets or keys; redact sensitive fields
- Enforce least-privilege access for all agents

Testing rules
- Unit tests for cache helpers; integration tests for end-to-end cache path
- Simulate cache latency, cache misses, and source latency

Deployment rules
- Role-based feature toggles; deploy cache logic with feature flags
- Canary or canary-like rollout for cache policy changes

Human review and escalation rules
- Any data inconsistency or TTL anomaly triggers human review

Failure handling and rollback rules
- On cache failure, gracefully fall back to source with degraded performance signal
- Rollback to previous TTL and policy version if a release causes issues

Things Agents must not do
- Do not bypass caching policies; do not write to external systems without approval
- Do not mutate data in transit without validation
- Do not drift from the canonical data source without logging a handoff

Recommended Agent Operating Model

Roles and decision boundaries are designed for robust multi-agent collaboration in a read-through cache workflow. The Planner makes high-level decisions, the Implementer executes the path, the Reviewer validates, and the Tester confirms end-to-end behavior. The Researcher and Domain Specialist ensure policy correctness and data-domain compliance. Escalation paths ensure human review when data integrity or security risk is detected.

Recommended Project Structure

ai-readthrough-cache/
  agents/
    planner/
    implementer/
    reviewer/
    tester/
    researcher/
    domain-specialist/
  orchestrator/
  policies/
  src/
  tests/
  docs/

Core Operating Principles

Clear ownership and boundaries for each agent role
Idempotent, deterministic actions with auditable traces
Explicit handoffs and escalation paths
Single source of truth for data and cache state
Observability, tracing, and validation at every step

Agent Handoff and Collaboration Rules

Planner communicates policy, outputs planned cache path and TTL adjustments; Implementer executes the plan; Reviewer validates, then Tester performs integration checks; Researcher and Domain Specialist refine TTL and domain constraints; escalation to humans occurs on anomalies.

Tool Governance and Permission Rules

Commands and API calls must be authorized; secrets never hard-coded; production systems require approvals and change-management gates; all tool usage is logged and auditable.

Code Construction Rules

Follow language- and framework-appropriate patterns; ensure idempotence, error handling, and clear interfaces between plan, execution, and validation steps. Include tests and mocks for all external systems.

Security and Production Rules

Enforce least-privilege, rotate credentials, redact sensitive data in logs, and validate data privacy constraints; use feature flags for production changes; monitor for drift and abnormal cache misses.

Testing Checklist

Unit tests for cache helpers and policy evaluation
Integration tests for planner-implementer-reviewer interactions
End-to-end tests simulating cache misses and source latency
Security tests for secrets handling and access control

Common Mistakes to Avoid

Overcomplicating the planner with too many heuristics
Unclear handoff boundaries causing role confusion
Neglecting data freshness and TTL policy in multi-agent flows
Allowing unauthorized access to cache or secret stores

FAQ

What is the purpose of this AGENTS.md Template for read-through cache architecture?

To provide a complete, copyable operating manual that governs single and multi-agent workflows for a read-through cache, including roles, handoffs, memory rules, tool governance, and security considerations.

How are agent handoffs managed in this workflow?

Handoffs follow a defined sequence: Planner → Implementer for path execution, Implementer → Reviewer for validation, Reviewer → Tester for integration checks, with Researcher and Domain Specialist advising TTL and domain rules as needed.

How is data freshness and TTL enforced across agents?

TTL policies are declared in the Planner and enforced by the Implementer; the Reviewer cross-checks TTL correctness against the source-of-truth; domain constraints from the Domain Specialist ensure data validity.

What are the security considerations for tool access and secrets?

Access is least-privilege, secrets are vault-managed with rotation, and logs redact sensitive fields; production changes require approvals and change management gates.

What should a team do if the read-through cache fails?

Fallback to the source with a controlled degradation signal, log the incident, and trigger a human review if the failure persists or indicates data inconsistency.

Target User

Use Cases