Environment Promotion AGENTS.md Template for AI coding agents

Overview

This AGENTS.md Template for environment promotion workflows codifies how AI coding agents orchestrate changes across environments (dev, staging, prod). It supports both single-agent execution and multi-agent orchestration with clear handoffs, memory, and sources of truth. It provides a concrete operating context that teams can paste into an AGENTS.md file to govern environment promotion work.

Direct answer: This template defines roles, rules, and escalation paths for safe, auditable environment promotions using AI coding agents.

When to Use This AGENTS.md Template

• When implementing automated or semi-automated environment promotion pipelines (dev → staging → prod).
• When you need explicit agent handoffs, governance, and human review checkpoints.
• When multi-agent coordination is required to validate tests, approvals, and deployment steps.
• When you want a shareable, copyable project-level operating manual for agent behavior.

Copyable AGENTS.md Template

# AGENTS.md
# Environment Promotion AGENTS.md Template
# Project context
Project role: Environment promotion pipeline for AI coding agents across dev, staging, and prod
Agent roster and responsibilities:
- Planner: defines promotion policy, gates, and sequence
- Implementer: applies changes to target environment and artifact updates
- Reviewer: validates changes, tests, and policy conformance
- Tester: executes automated and manual tests in staging
- Researcher: sources required data, docs, and runbooks
- Domain Specialist: ensures domain constraints and compliance
Supervisor or orchestrator behavior:
- The Orchestrator orchestrates multi-agent steps, enforces gates, stores memory, and enacts handoffs
- Maintains a single source of truth for artifacts, test results, and approvals
Handoff rules between agents:
- Planner -> Implementer: plan and summarize changes with acceptance criteria
- Implementer -> Reviewer: present changes, tests, and evidence
- Reviewer -> Tester: authorize test run and verify outcomes
- Tester -> Orchestrator: report pass/fail and trigger rollback if needed
- Researcher/Domain Specialist -> Planner: provide constraints and updates to policy
Context, memory, and source-of-truth rules:
- All decisions are linked to a versioned artifact + test result set + code changes
- Memory is stored in a central workspace with artifacts tagged by environment and version
- Source-of-truth includes version control commits, CI/test results, issue trackers, and runbooks
Tool access and permission rules:
- Agents may read all environment configs and tests, but write access is restricted by policy gates
- Secrets are accessed via a vault, not embedded in code or logs
- Production changes require explicit approvals and pre-deployment checks
Architecture rules:
- Maintain a layered promotion pipeline with explicit gates and rollbacks
- Use a centralized orchestrator for multi-agent coordination
- All changes are versioned and auditable
File structure rules:
- Keep AGENTS.md at project root
- Store artifacts, test results, and logs under env-promo//
Data, API, or integration rules:
- Interactions with CI/CD, artifact registries, and monitoring must pass defined tests
- All integrations must be versioned and reversible
Validation rules:
- All gates must pass before promotion proceeds
- Validation must include functional, integration, security, and compliance tests
Security rules:
- Never expose production credentials in logs
- Validate all inputs and sanitize data
- Enforce least privilege for tool access
Testing rules:
- Run unit, integration, and end-to-end tests in staging before prod promotion
- Include health checks and rollback verification
Deployment rules:
- Deployments must be auditable and reversible
- Promote only after green status across tests and approvals
Human review and escalation rules:
- If a gate fails, trigger escalation to the domain specialist and project lead
- Provide a backup rollback plan and notify stakeholders
Failure handling and rollback rules:
- Rollback to prior artifact and database state if promotion fails
- Preserve idempotence and ensure redeployment is safe
Things Agents must not do:
- Do not bypass gates or approvals
- Do not mutate production without testing and rollback plans

Recommended Agent Operating Model

The operating model defines roles, decision boundaries, and escalation paths for environment promotion. It supports both single-agent execution and multi-agent coordination with clear handoffs and accountability.

• Planner decides policy, gates, and progression criteria
• Implementer performs changes within policy constraints
• Reviewer certifies conformance to policy and tests
• Tester validates behavior in staging and records results
• Researcher supplies data, docs, and runbooks
• Domain Specialist ensures domain-specific controls and compliance
• Orchestrator coordinates handoffs, enforces gates, and maintains memory

Recommended Project Structure

environment-promo/
  orchestrator/
  agents/
    planner/
    implementer/
    reviewer/
    tester/
    researcher/
    domain-specialist/
  policies/
  tests/
  docs/
  config/
  scripts/

Core Operating Principles

• Maintain auditable trails for all promotions
• Enforce least privilege and secret handling
• Ensure explicit handoffs with evidence and criteria
• Isolate environment-specific logic to prevent drift
• Prefer idempotent operations and safe rollbacks

Agent Handoff and Collaboration Rules

• Planner to Implementer: pass plan, criteria, and evidence
• Implementer to Reviewer: provide diffs, tests, and docs
• Reviewer to Tester: authorize staging validation
• Tester to Orchestrator: report status and potential blockers
• Researcher and Domain Specialist to Planner: update constraints and guardrails

Tool Governance and Permission Rules

• Only orchestrator and planner can modify gates
• Artifacts stored in versioned registries with access controls
• Secrets accessed via vaults, not in logs or code
• All API calls recorded and auditable
• Approvals required before prod deployment

Code Construction Rules

• Write idempotent scripts for promotions
• Document every step and rationale
• Validate dependencies and environment parity
• Avoid hard-coding environment-specific values

Security and Production Rules

• Protect production data; sanitize and redact where necessary
• Enforce role-based access control for all agents
• Audit all changes and maintain evidence for audits

Testing Checklist

• Unit tests for promotion steps
• Integration tests for deployment pipelines
• End-to-end tests in staging with rollbackability
• Security and compliance validations

Common Mistakes to Avoid

• Skipping tests or approvals
• Drift between environments
• Hiding failures or inconsistent logs
• Overly broad privileges in tool access

Related implementation resources: AI Use Case for Sales Pipeline Reviews and Deal Risk Scoring and AI Use Case for Policy Documents and Internal Question Answering.

FAQ

What is this environment promotion AGENTS.md Template for?

It provides a copyable operating manual for AI coding agents to promote code and infra changes across environments with governance.

Which agent roles are defined?

Planner, Implementer, Reviewer, Tester, Researcher, Domain Specialist, and an Orchestrator

How are promotions gated and approved?

Promotions pass gates for tests, checks, and explicit human approvals before prod deployment

What are memory and source-of-truth rules?

Memory is centralized; source-of-truth includes artifacts, test results, logs, and runbooks

What should agents not do?

Do not bypass gates, reveal secrets in logs, or perform unsanctioned changes to production