Eventual Consistency AGENTS.md Template

Overview

This AGENTS.md template governs the eventual consistency design workflow for AI coding agents. It provides a comprehensive operating manual that supports both single-agent and multi-agent orchestration, ensuring clear roles, governance, and escalation paths.

Direct answer: This template defines a repeatable, auditable pattern for building, validating, and recovering from divergent states across distributed systems with AI agents.

When to Use This AGENTS.md Template

• You need an auditable protocol for data reconciliation across distributed services.
• You want explicit handoffs and clear decision boundaries between agents (ingest, sync, conflict resolver, validator, monitor).
• You're introducing multi-agent orchestration where consistency may lag behind real-time events.
• You require governance for tool access, secrets, and production workflows with human review gates.

Copyable AGENTS.md Template

# AGENTS.md

Note: AGENTS.md template for eventual consistency design.

Project Role: Eventual Consistency Platform Owner and Orchestrator for cross-service data reconciliation.

Agent roster and responsibilities:
- IngestAgent: read events from source, normalize payloads, emit canonical events.
- SyncAgent: apply events to downstream systems, enforce idempotency, and publish updated state to memory.
- ConflictResolverAgent: detect divergent states, apply reconciliation policy, and record decisions.
- ValidatorAgent: validate payloads against invariant rules and schemas before persistence.
- MonitorAgent: collect metrics, detect drift, and trigger alerts when thresholds exceed.
- OrchestratorAgent: coordinates planning, handoffs, and retries; maintains run context and checkpoints.

Supervisor / Orchestrator behavior:
- Plan, delegate, and observe; replan on errors; escalate when automated recovery fails.
- Ensure end-to-end traceability from source to target state.

Handoff rules between agents:
- IngestAgent > SyncAgent: after canonicalized event is produced and validated by ValidatorAgent.
- SyncAgent > ConflictResolverAgent: when divergence is detected by monitoring or after apply attempts.
- ConflictResolverAgent > ValidatorAgent: upon applying reconciled state before final commit.
- All agents > OrchestratorAgent: update status, logs, and checkpoints.

Context, memory, and source-of-truth rules:
- Run context is append-only; source-of-truth is the canonical data store.
- Memory stores are used to track events, decisions, and reconciliations with timestamps.
- Never rely on transient caches as truth; always reference the source-of-truth for reconciliation.

Tool access and permission rules:
- Agents have scoped access to databases, queues, and APIs necessary for their role.
- Secrets stored in a vault; access is role-based and audited.
- No direct access to production systems without approval gates.

Architecture rules:
- Event-driven, eventual consistency with idempotent operations.
- Use deterministic reconciliation policies and deterministic conflict handlers.
- All changes must be auditable and replayable.

File structure rules:
- agents/ingest/
- agents/sync/
- agents/conflict/
- agents/validate/
- agents/monitor/
- orchestrator/
- memory/
- config/
- tests/

Data, API, or integration rules:
- Use stable APIs with versioned contracts; validate inputs and outputs.
- Validate schema on every boundary; reject invalid payloads.

Validation rules:
- All state transitions must be deterministic given input and policy.
- Validation must occur before writing to canonical stores.

Security rules:
- Least privilege access; secrets kept in vault; all actions auditable.
- Avoid leaking internal identifiers in responses.

Testing rules:
- Unit tests per agent, integration tests for end-to-end flow, and end-to-end replay tests.
- Mock external services; verify idempotency and convergence.

Deployment rules:
- CI/CD gates; feature flags for risk-controlled rollout; canary deployments for new agents.

Human review and escalation rules:
- Automatic escalation to a human when automated recovery fails after N retries.
- Reviewer required for policy changes and reconciliation strategies.

Failure handling and rollback rules:
- If an apply fails, roll back to the previous committed state, and replay until converged.
- Maintain an auditable rollback log.

Things Agents must not do:
- Do not mutate source-of-truth directly without orchestration approval.
- Do not bypass validation or caching layers.
- Do not reveal secrets via log messages or outputs.

Recommended Agent Operating Model

Roles, responsibilities, decision boundaries, and escalation paths are defined to balance automation with governance. In eventual consistency design, the Orchestrator coordinates plan and retries, while specialized agents perform domain-specific actions with clear handoffs and verifiable outcomes.

Recommended Project Structure

project-root/
  agents/
    ingest/
    sync/
    conflict/
    validate/
    monitor/
  orchestrator/
  memory/
  config/
  tests/
  docs/

Core Operating Principles

• Idempotent and deterministic actions across all agents.
• Strict source-of-truth for reconciliation and drift detection.
• Explicit handoffs with traceable context between agents.
• Least privilege and auditable changes with human review gates.
• Continuous validation and rollback capability for safety.

Agent Handoff and Collaboration Rules

• Planner defines plan scope and assigns tasks to Implementers (Ingest, Sync, Conflict, Validate).
• Reviewer validates policy changes and reconciliation rules before rollout.
• Tester runs end-to-end tests and monitors post-deploy drift.
• Researcher can propose new reconciliation strategies; Domain Specialist reviews impact on data invariants.
• Handoff includes run context, memory pointers, and source-of-truth references.

Tool Governance and Permission Rules

• Tools accessed via tightly-scoped APIs with RBAC and secrets vault integration.
• All tool calls are logged and auditable; cannot bypass approval gates.
• Secrets never appear in outputs; rotation and revocation policies enforced.

Code Construction Rules

• All changes are versioned; builds are reproducible; tests pass before merge.
• Follow deterministic input validation and output contracts at every boundary.
• Avoid hard-coding environment-specific values; use configuration and feature flags.

Security and Production Rules

• Protect data in transit and at rest; use encryption at rest and TLS in transit.
• Enforce sandboxed execution for agents; monitor for anomalous behavior.
• Maintain incident response runbooks and runbooks for rollback and escalation.

Testing Checklist

• Unit tests for each agent; mock external services.
• Integration tests verifying end-to-end eventual convergence.
• Deployment tests with feature flags; canary testing.

Common Mistakes to Avoid

• Over-optimistic assumptions about convergence speed without monitoring.
• Skipping validation before applying to canonical stores.
• Disabling audit trails or skipping escalation gates.

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 Intermodal Transport Providers Using Rail Schedules To Coordinate Seamless Truck-To-Train Transfers.

FAQ

What is the purpose of this AGENTS.md Template for eventual consistency design?

It provides a copyable operating manual that defines agent roles, handoffs, governance, and recovery for eventual consistency with AI coding agents.

How do agents hand off tasks in this design?

The orchestrator assigns tasks and each handoff includes run context, source-of-truth references, and a validated payload before transition.

What are memory and source-of-truth rules?

Memory is append-only for run context; the source-of-truth is the authoritative data store used for reconciliation.

How is tool access restricted?

Agents have scoped permissions; secrets reside in a vault with auditing; production actions require approval gates.

How are failures handled and rolled back?

On failure, roll back to the last known good state and replay; escalate to human review if needed.