AGENTS.md Template for Multi Leader Replication Design

Overview

Direct answer: This AGENTS.md Template for multi leader replication design defines the operating context for AI coding agents coordinating a multi-leader replication workflow. It supports both single-agent execution and multi-agent orchestration across planner, implementer, reviewer, researcher, and domain specialist roles.

When to Use This AGENTS.md Template

• When designing and validating a multi leader replication pattern in distributed databases
• When you need formal handoff rules, memory/context governance, and a single source of truth
• When coordinating multiple agents across design, implementation, testing, and deployment

Copyable AGENTS.md Template

# AGENTS.md

Project role: Design and operate a multi leader replication workflow for AI coding agents in a distributed database environment.

Agent roster and responsibilities:
- Planner: defines design goals, data flows, and topologies; coordinates with domain specialist for schema and constraints; outputs the design plan to Implementer.
- Implementer: translates the plan into configurations, scripts, and artifacts; enforces architecture rules and idempotence; prepares validation artifacts.
- Reviewer: validates architecture, configurations, and test results; checks security, compliance, and performance envelopes; approves changes for staging.
- Researcher: gathers data on latency, conflict scenarios, and replication edge cases; proposes mitigation strategies; feeds insights to Planner and Domain Specialist.
- Domain Specialist: ensures domain-specific constraints, data sovereignty, regulatory requirements, and compliance considerations are honored in all artifacts.

Supervisor or orchestrator behavior:
- The Orchestrator coordinates task assignment, tracks state, and merges results from all agents; enforces handoff rules and approval gates.
- Maintains a single source of truth in the project docs and configs repo; memos and decisions are stored as artifacts for traceability.

Handoff rules between agents:
- Planner to Implementer: deliver design doc and topologies; acceptance criteria include architecture integrity and test plan.
- Implementer to Reviewer: deliver configs, scripts, and validation results; acceptance criteria include passes of test suites and security checks.
- Reviewer to Orchestrator: confirm approvals; record decisions and rationale.
- Researcher and Domain Specialist to Planner: deliver insights and constraints; update design as needed.

Context, memory, and source-of-truth rules:
- Central source-of-truth is the docs repository and configs repo; memory is ephemeral per task but decisions are logged with timestamps.
- All artifacts must reference source data and relevant references; no orphan data.

Tool access and permission rules:
- Planner can read the design docs and docs repository; Implementer can modify configs and code; Reviewer can modify tests and security checks; Domain Specialist and Researcher have read access to all artifacts.
- Production changes require an explicit approval gate and audit log entries.

Architecture rules:
- Prefer a multi leader replication topology with strict conflict resolution and deterministic conflict handling.
- Use idempotent change scripts and schema migrations; ensure backward compatibility where possible.

File structure rules:
- Place artifacts under a conventional layout with docs, configs, scripts, and tests; avoid unrelated folders.

Data, API, or integration rules:
- Do not embed secrets in code; use a secret manager; refer to endpoints by alias names in configs.

Validation rules:
- Run unit and integration tests; validate replication lag and data consistency under simulated failure.

Security rules:
- Enforce TLS for all inter-node communication; rotate keys; store secrets in a manager; audit all access.

Testing rules:
- Include unit tests for individual components, integration tests for replication flows, and deployment tests for rollbacks.

Deployment rules:
- Deploy changes to staging before production; require green validations before promoting.

Human review and escalation rules:
- All safety and data-sensitivity decisions require human review; escalate to architecture review for non-deterministic outcomes.

Failure handling and rollback rules:
- Maintain a snapshot of previous configurations; provide a rollback script for rapid restoration; alert on divergence thresholds.

Things Agents must not do:
- Do not bypass approval gates; do not perform unsupervised production changes; do not duplicate work across artifacts.

Recommended Agent Operating Model

Roles, responsibilities, decision boundaries, and escalation paths are defined to enable effective multi-agent coordination for multi leader replication design. The Planner owns the design intent; the Implementer executes within constraints; the Reviewer validates; the Researcher probes edge cases; the Domain Specialist enforces domain constraints. Escalation paths exist from Implementer to Reviewer to Orchestrator when blockers arise, with a defined SLA per stage.

Recommended Project Structure

/multi-leader-replication
  /agents
    /planner
    /implementer
    /reviewer
    /researcher
    /domain-specialist
  /orchestrator
  /configs
  /data
  /docs
  /tests
  /scripts

Core Operating Principles

• Clarity over ambiguity in design artifacts and handoffs
• Single source of truth for decisions and versions
• Defensive programming and explicit failure handling
• Traceability with auditable decisions and rationale
• Security and access controls built into every step

Agent Handoff and Collaboration Rules

• Planner produces a design doc with topology, data flows, and constraints; Implementer must produce working configs meeting those constraints.
• Handoffs require artifacts and acceptance criteria; time-bound checkpoints are mandatory.
• Researchers and Domain Specialists must provide constraints early to avoid late-stage changes.

Tool Governance and Permission Rules

• Actions on production systems require explicit approvals and rollback capability
• Secrets stored only in a secrets manager; scripts reference aliases not concrete credentials
• All API calls and file edits are auditable

Code Construction Rules

• Write idempotent scripts and migrations
• Use deterministic sequencing for topology changes
• Avoid hard-coding endpoints; use configuration references

Security and Production Rules

• Encrypt data in transit and at rest; rotate credentials regularly
• Limit privileges to what is strictly necessary
• Audit all production changes and ensure rollback capability

Testing Checklist

• Unit tests for individual components
• Integration tests for replication flows and conflict resolution
• Deployment tests for rollback and recovery

Common Mistakes to Avoid

• Skipping early validation of topology and data flows
• Missing explicit handoff criteria
• Bypassing security gates or secret management

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 for multi leader replication design?

It provides a concrete operating manual for planning, implementing, and governing a multi-leader replication workflow with AI coding agents.

Who are the primary agents and what are their responsibilities?

Planner defines design goals and data flows; Implementer builds config/scripts; Reviewer validates architecture and tests; Researcher investigates latency and conflict scenarios; Domain Specialist enforces domain constraints.

How are handoffs between agents managed?

Handoff rules specify artifacts and acceptance criteria at each stage with time-bound checkpoints and approval gates.

What are the security and tool governance rules?

Secrets must be stored in a secrets manager; only approved APIs and tools may be invoked; changes require approval gates and audit logging.

How is testing and rollback handled in production?

Automated tests cover unit, integration, and deployment checks; on failure, rollback scripts restore prior configurations and notify stakeholders.