AGENTS.md Template for Cache Aside Pattern Design

Overview

Direct answer: This AGENTS.md template provides a concrete operating manual for cache-aside pattern design using AI coding agents and multi-agent orchestration.

The AGENTS.md template governs the roles, responsibilities, and governance for both single-agent workflows and multi-agent orchestration in the cache-aside pattern. It defines how agents collaborate to read from, write to, and invalidate a cache in coordination with the source of truth to ensure consistency and performance.

When to Use This AGENTS.md Template

• When you want a repeatable, auditable operating context for automating cache reads and writes with AI coding agents.
• When your system uses a cache-aside pattern and requires clear agent roles, handoffs, and governance.
• When reliability, traceability, and security are critical across multiple agent teams and environments.
• When you need explicit memory, source-of-truth rules, and rollback paths in production deployments.

Copyable AGENTS.md Template

# AGENTS.md
Project Role: Platform Engineering Lead for Cache-aside Orchestration
Agent Roster and Responsibilities:
- Planner Agent: defines goals, milestones, and task decomposition for cache orchestration.
- Cacher Agent: performs read/write operations against the cache, enforces TTLs, and coordinates with the home data source.
- DataSource Agent: queries the system of record and returns canonical values for cache warms and refreshes.
- Invalidation Agent: invalidates stale cache entries based on data changes or TTL expiration.
- Validator Agent: validates outputs, ensures data types and TTLs meet policy, and guards against data leaks.
- Orchestrator Agent: supervises the workflow, sequences handoffs, and escalates to humans on risk.
- Reviewer Agent: provides human review guidance and approves deployments to production.
- Logger Agent: emits structured traces to a centralized memory store for audit.

Supervisor / Orchestrator Behavior:
- The Orchestrator assigns tasks, sequences handoffs, and enforces decision boundaries.
- Outputs from one agent are passed to the next with explicit memory keys and source-of-truth references.
- If any step fails, the Orchestrator triggers a controlled rollback and alert.

Handoff Rules:
- Planner -> DataSource: share goals and inputs; DataSource returns canonical value.
- DataSource -> Cacher: cacheable value and TTL; Cacher writes to cache and updates memory.
- Cacher -> Invalidation: if writes occur, schedule invalidation checks; Invalidation clears stale entries.
- Invalidation -> Validator: verify cache correctness and non-null outputs.
- Validator -> Orchestrator: signal completion or escalate on risk.
- Reviewer provides human review input and final approval for production.

Context, Memory & Source of Truth:
- Memory carries task context, inputs, and outputs for each step; memory is cleared on completion unless retention is required for auditing.
- Source of Truth is the system of record; cache values must be derived from it and treated as read-through only in absence of a fresh value.
- All outputs must include a memory snapshot and a reference to the source of truth used.

Tool Access & Permissions:
- Access to cache, data source, and validation endpoints is restricted to agents in the roster with environment-scoped tokens.
- Secrets are retrieved from a vault; do not embed secrets in memory or logs.
- Production system calls require approval by the Orchestrator and governance gates.

Architecture Rules:
- Use modular microservices; agents communicate via structured messages; avoid tight coupling.
- All components log to a central tracing store with correlation IDs.
- Idempotent operations are required for writes to the cache.

File Structure Rules:
- Place all agent definitions under agents/, with subfolders per role.
- Avoid duplicating work across agents; reuse shared utilities from common/ if needed.

Data, API, or Integration Rules:
- Interactions with system of record must be read-only unless an explicit approval exists.
- Cache writes must be accompanied by a version or timestamp.

Validation Rules:
- Validate data shapes, TTL values, and required fields on every step.
- Ensure outputs are deterministic given the same inputs.

Security Rules:
- Never store secrets in code or logs; use vault and short-lived tokens.
- Enforce network isolation and least privilege access.

Testing Rules:
- Unit tests for each agent, integration tests for the orchestration, and end-to-end tests for critical flows.
- Mock external systems in tests.

Deployment Rules:
- Changes pass CI checks, run tests, and require a sign-off before production deployment.
- Deployment triggers can only originate from the Orchestrator.

Human Review and Escalation Rules:
- If confidence falls below a threshold, escalate to a human reviewer and pause deployment.
- All escalations must be auditable.

Failure Handling and Rollback Rules:
- On failure, revert cache to previous stable state and re-run the last successful steps with adjusted inputs.
- Log root cause and trigger a post-mortem.

Things Agents Must Not Do:
- Do not bypass the Orchestrator or bypass gates.
- Do not exfiltrate secrets or write to production data without approval.
- Do not drift context; maintain alignment with the source of truth.

Notes:
- This template is designed for a cache-aside pattern delivery with AI coding agents. Use the same ROBO-ops approach across environments.

Recommended Agent Operating Model

The agent roles defined above operate within a clearly bounded decision space. The Planner hands off to the DataSource and Cacher in a deterministic sequence, with the Orchestrator maintaining global state and ensuring traceability. Escalations flow to human review when risk is detected. Agents operate with explicit memory keys and adhere to source-of-truth rules to prevent drift.

Recommended Project Structure

cache-aside-pattern/
  orchestrator/
  agents/
    planner/
    data-source/
    cacher/
    invalidator/
    validator/
    reviewer/
  memory/
  configs/
  tests/
  docs/

Core Operating Principles

• Single source of truth: rely on the system of record for authoritative data.
• Idempotent actions: repeated runs must have the same effect as a single run.
• Traceability: every step emits structured logs with correlation IDs.
• Explicit memory: preserve context only as long as needed for the workflow.
• Least privilege: agents operate with minimum necessary permissions.

Agent Handoff and Collaboration Rules

• Planner to DataSource: share goals, inputs, and constraints.
• DataSource to Cacher: provide canonical value and TTL guidance.
• Cacher to Invalidation: trigger invalidation when writes occur or TTL expires.
• Invalidation to Validator: verify cache correctness and non-null outputs.
• Validator to Orchestrator: approve completion or escalate on risk.
• Reviewer provides human review input and final approval for production.

Tool Governance and Permission Rules

• Command execution restricted to registered agents with vault-backed credentials.
• File edits require orchestrator approval; avoid direct production file changes by agents.
• API calls must go through governed endpoints with strict rate limits and auditing.
• Secrets must never be logged or embedded in code; use encrypted storage.
• Production systems require approval gates and change management practices.
• External service access must be whitelisted and monitored.
• All actions must be auditable and traceable through logs and memory.

Code Construction Rules

• Use deterministic functions; avoid non-deterministic randomness in critical paths.
• Prefer explicit data contracts; validate inputs and outputs at every boundary.
• Keep functions pure where possible; minimize side effects in the caching workflow.
• Document interface schemas in a shared place; ensure backward compatibility.
• Follow language- and framework-specific style guides; write tests alongside code.

Security and Production Rules

• Secrets stored in vaults; tokens are short-lived and rotated regularly.
• Network security: only allow required ports and endpoints; deny broad egress.
• Audit trials: maintain immutable logs for all cache interactions and governance events.
• Feature flags: control risky changes behind feature gates.

Testing Checklist

• Unit tests covering each agent’s logic and contracts.
• Integration tests validating cross-agent handoffs and memory state.
• End-to-end tests simulating production-like cache reads/writes and invalidations.
• Performance tests for cache warm-up and TTL handling.
• Security tests for secret handling and access control.

Common Mistakes to Avoid

• Skipping explicit handoff contracts between agents.
• Allowing drift between cache and source of truth without validation.
• Bypassing the orchestrator or approval gates for production changes.
• Overloading memory with large, unbounded context.
• Ignoring security reviews and secret management policies.

Related implementation resources: AI Use Case for Corporate Event Managers Using Slack To Orchestrate Day-Of Venue Tasks Across Multi-Department Teams and AI Agent Use Case for Wholesalers Using Multi-Currency Ledger Trackers To Calculate Foreign Exchange Risk Exposure Across Global Accounts.

FAQ

What is the purpose of this AGENTS.md Template?

It provides a concrete operating manual for configuring AI coding agents to implement a cache-aside pattern, including roles, handoffs, and governance.

Which agents are involved in the cache aside workflow?

Planner, CacheAgent, DataSourceAgent, InvalidationAgent, ValidatorAgent, and ReviewerAgent orchestrated by the Orchestrator.

How are handoffs between agents governed?

Handoffs follow a defined sequence with explicit memory and source-of-truth references; agents produce outputs consumed by the next agent.

What are the security and production constraints?

Secrets never stored in code; access limited to configured services; changes require review; production changes require approval gates.

What happens on failure or rollback?

If an agent fails, the orchestrator triggers rollback to a previous cache state and notifies human review.