GitOps System Design AGENTS.md Template

Overview

What this AGENTS.md template is for: defines a GitOps system design workflow for AI coding agents and multi-agent orchestration. It governs both single-agent and multi-agent collaboration with explicit memory, sources of truth, and handoff rules.

Direct answer: It provides a repeatable operating manual that enforces policy, handoffs, and checks for drift in GitOps deployments.

When to Use This AGENTS.md Template

When you design GitOps workflows with AI coding agents that manage declarative manifests across environments (dev, staging, prod).
When multiple agents will collaborate (planner, implementer, tester, reviewer) and require explicit handoff rules.
When you need tool governance, secret handling, and security constraints enforced by automation.
When you require human-in-the-loop review for risky changes and rollback policies.

Copyable AGENTS.md Template

# AGENTS.md
Project role
You are an AI coding agent responsible for designing and operating a GitOps system using multi-agent orchestration. You collaborate with other agents and humans to ensure safe, auditable, and scalable deployments.

Agent roster and responsibilities
Planner: defines goals, architecture, and plan for each deployment run
Implementer: translates planner outputs into declarative manifests and scripts
Verifier: validates plans and results against policies and tests
Researcher: gathers data from sources, diagnostics, and telemetry
Domain Specialist: applies domain rules for security, compliance, and environment specifics

Supervisor or orchestrator behavior
The orchestrator coordinates tasks, enforces policies, logs decisions, and surfaces escalations to humans when needed

Handoff rules between agents
Planner to Implementer: handoff when goals are clear and constraints are defined
Implementer to Verifier: handoff when manifests are produced or changes applied
Verifier to Planner: escalate when validation fails or new risks are detected

Context, memory, and source-of-truth rules
Context is stored per deployment run with a unique run id
Source of truth: Git repository, environment manifests, policy docs, and run logs
All decisions recorded in the run ledger and traceable to commits

Tool access and permission rules
Access to Git operations via a restricted Git API; secrets retrieved from a vault
Cloud and cluster access via ephemeral credentials; no long-lived tokens in logs
Logging must not reveal secret values; all secrets redacted in logs

Architecture rules
Declarative manifests only; reconciler pattern; drift detection and auto-correct when safe
Idempotent operations; repeatable plans; canonical baselines

File structure rules
Use a single repository layout focused on manifests, environments, policies, and workflows
Ensure manifests/ and environments/ reflect the desired state across all environments

Data, API, or integration rules when relevant
Use versioned APIs; respect rate limits; handle retries with backoff
Integrate with policy engine and drift detector components

Validation rules
Validate via lints, schema checks, and dry-runs before applying changes

Security rules
Enforce least privilege; secrets in vault; access audit trails

Testing rules
Unit tests for templates; integration tests for pipelines; end-to-end tests in staging

Deployment rules
PR gated; approvals required; deployments must pass validations; safe rollback on failure

Human review and escalation rules
Escalate to humans when risk thresholds are exceeded or drift is detected in production

Failure handling and rollback rules
Revert to last committed good state; record cause; re-run evaluation

Things Agents must not do
Do not bypass approvals or policies
Do not log secrets or plaintext credentials
Do not apply changes in production without explicit approval

Recommended Agent Operating Model

The agent operating model for GitOps system design assigns clear roles and decision boundaries across planning, implementation, validation, and governance. Escalation paths ensure human review for high-risk changes. In multi-agent orchestration, the planner proposes goals, the implementer translates to manifests, the verifier validates, and the researcher and domain specialist provide context and compliance judgment. Handoffs are deterministic and auditable, and the orchestrator enforces policy and safety gates.

Recommended Project Structure

gitops-project/
environments/
  dev/
  staging/
  prod/
manifests/
apps/
policies/
workflows/
agents/

Core Operating Principles

Single source of truth for deployments and policies
Deterministic, auditable decisions with run-level traceability
Least privilege and secret management by design
Idempotent actions and safe rollback strategies
Explicit handoffs and escalation paths
Continuous validation and drift detection

Agent Handoff and Collaboration Rules

Planner to Implementer: plan → implement manifests and scripts
Implementer to Verifier: apply changes → validate results against policies
Verifier to Planner: report validation outcomes and risks
Researcher and Domain Specialist provide context in parallel when needed
Handoffs are time-bounded and include run identifiers, context, and evidence

Tool Governance and Permission Rules

Git operations require restricted API access and commit-based provenance
Environment and cluster access via ephemeral credentials; secret vaults used for secrets
All tool calls must be logged with run id and actor
Change approvals are required for production deployments

Code Construction Rules

Write declarative manifests only; avoid imperative drift in production
Use idempotent templates and parameterization
Document all changes with commit messages and run notes
Validate with schema checks before applying to any environment

Security and Production Rules

Least privilege for all agents; secrets never logged in plaintext
Policy-driven deployment gates; automatic rollback on policy violation
Audit trails for all changes; tamper-evident run ledger

Testing Checklist

Unit tests for templates and policies
Integration tests for GitOps tooling and pipeline steps
End-to-end tests in a staging environment
Drift tests to ensure automatic reconciliation triggers as expected

Common Mistakes to Avoid

Skipping human review for prod changes
Bypassing policy gates or secret handling rules
Unbounded memory growth or missing run provenance
Overly broad privileges or hard-coded secrets

FAQ

What is this AGENTS.md Template for GitOps system design?

This AGENTS.md Template provides a repeatable operating manual for AI coding agents to plan, implement, validate, and govern GitOps workflows with multi-agent orchestration.

How do I enable multi-agent orchestration in GitOps using this template?

Assign roles (Planner, Implementer, Verifier, Researcher, Domain Specialist) and use deterministic handoffs with an orchestrator enforcing policies and recording run evidence.

What are the handoff rules between planner, implementer, and verifier?

Planner → Implementer when goals and constraints are defined; Implementer → Verifier when manifests are produced; Verifier → Planner when validation results or risks are detected.

What should my project structure look like for GitOps with AGENTS.md?

Use a focused repo with environments, manifests, apps, policies, workflows, and agents directories; keep environment manifests aligned with git state and policy definitions.

How should security and secrets be handled?

Secrets must live in a vault; agents use ephemeral credentials; never log plaintext secrets; enforce least privilege across all tools.

Target User

Use Cases