AGENTS.md Template: Order Management Architecture for AI Coding Agents

Overview

Direct answer: This AGENTS.md Template defines a concrete operating manual for an order management architecture that enables single-agent and multi-agent orchestration across the order lifecycle. It governs order intake, inventory checks, payment processing, fulfillment, and customer communications, while providing clear handoffs and governance for AI coding agents.

The template is designed so individual agents can operate with well-defined boundaries and so a supervisor/orchestrator can coordinate multiple agents to handle complex orders with reliability and traceability.

When to Use This AGENTS.md Template

• Deploy a multi-agent order lifecycle: order capture, inventory validation, payment, fulfillment, and post-order notifications.
• Govern access to tools and data sources across inventory systems, payment gateways, warehouse systems, and OMS APIs.
• Enable reproducible handoffs, traceable decision points, and auditable state transitions for complex orders.
• Maintain human review gates for high-risk steps such as refunds, chargebacks, or inventory reservations.

Copyable AGENTS.md Template

# AGENTS.md

Project role
- OrderOps AI App: coordinates order lifecycle across agents

Agent roster and responsibilities
- Planner: designs workflow, decomposes tasks, and schedules agent actions
- InventoryAgent: validates stock, flags backorders, updates stock estimates
- PaymentAgent: handles payment intents, authorizations, and refunds
- FulfillmentAgent: triggers picking/packing and carrier handoffs
- NotifierAgent: communicates status updates to customers and systems
- ReconcilerAgent: reconciles post-order state with OMS and finances
- AuditorAgent: records audit trails and detects anomalies

Supervisor or orchestrator behavior
- Orchestrator receives new orders, enforces state machine, routes tasks to agents, and enforces escalation when needed

Handoff rules between agents
- Planner assigns tasks to specific agents and waits for completion signals
- Each agent writes to shared OrderContext with explicit versioning
- Handoffs require a confirmed result before moving to the next state

Context, memory, and source-of-truth rules
- Source of truth: OMS, Inventory, Payment gateway, and Fulfillment system
- Context lives in a centralized OrderContext keyed by order_id
- Memory is append-only, tagged with timestamps and agent names

Tool access and permission rules
- Agents can call only permitted APIs tied to their role
- Secrets are read from a vault; no agent stores secrets locally

Architecture rules
- Event-driven architecture with idempotent operations
- Stateless agents with short-lived in-memory caches
- All state transitions logged to an immutable audit log

File structure rules
- Keep agent code under order-management/agents with subfolders per role
- Central orchestrator under order-management/orchestrator
- Shared models under order-management/data

Data, API, or integration rules when relevant
- Use order_id as the primary key across systems
- Validate data from OMS and inventory before progression

Validation rules
- Each step must emit a success signal before the next step
- Implement compensating actions for failed steps

Security rules
- All credentials in a vault; access limited by role
- Ensure PCI-DSS or local compliance as required by gateway integrations

Testing rules
- Unit tests per agent and integration tests across the workflow
- Use synthetic orders to exercise end-to-end paths

Deployment rules
- Feature gates for new workflows; canary promotions for critical steps

Human review and escalation rules
- Escalate to human reviewer for refunds, large-value orders, or stock anomalies

Failure handling and rollback rules
- On failure, revert to last successful state and trigger a rollback plan

Things Agents must not do
- Do not mutate source-of-truth directly; always route through orchestrator
- Do not bypass handoffs or skip validation steps
- Do not retain secrets in memory or logs

Recommended Agent Operating Model

The model defines clear roles and decision boundaries for planners, implementers, reviewers, testers, researchers, and domain specialists. The Planner designs the end-to-end flow; Implementers codify agent capabilities; Reviewers validate outputs; Testers confirm reliability; Researchers provide external data and domain knowledge; Domain Specialists resolve edge cases such as complex pricing or returns policies. Escalation paths route issues to humans when criteria exceed automated bounds.

Recommended Project Structure

order-management/
  agents/
    planner/
    inventory/
    payments/
    fulfillment/
    notifier/
    reconciler/
    auditor/
  orchestrator/
  data/
    models/
    events/
  configs/
  workflows/
  tests/

Core Operating Principles

• Explicit handoffs with verifiable context
• Idempotent steps and auditable state
• Least privilege and tool governance
• Traceable decisions with single source of truth
• Fail-fast with clear rollback paths

Agent Handoff and Collaboration Rules

• Planner to Implementer handoffs require a task spec and acceptance criteria
• Implementers must publish status and evidence to the orchestrator
• Reviewer validates outputs before promotion to next stage
• Tester runs end-to-end tests, including error paths
• Researchers augment data sources as needed; Domain Specialist resolves edge cases

Tool Governance and Permission Rules

• Commands restricted by role; no direct production access by agents
• API access scoped per agent; secrets stored in vaults
• Production changes require orchestrator approval and audit trails

Code Construction Rules

• Write modular, idempotent agents with clear interfaces
• Tag changes with order_id context; avoid cross-order data leakage
• Maintain backward compatibility when updating agents

Security and Production Rules

• Encrypt data in transit and at rest; use signed messages between agents
• Rotate credentials; monitor for abnormal activity
• Respect data residency and PCI-DSS where applicable

Testing Checklist

• Unit tests for each agent; integration tests for the workflow
• End-to-end tests with synthetic orders; simulate failures
• Regression tests after changes to orchestrator or data models

Common Mistakes to Avoid

• Skipping explicit handoffs or bypassing the orchestrator
• Unevaluated state drift across systems
• Storing secrets in logs or non-secure memory
• Unbounded retries without backoff or limits

Related implementation resources: AI Agent Use Case for Apparel Wholesalers Using Regional Sales Metrics To Rebalance Inventory Across Distributed Fulfillment Nodes and AI Use Case for Corporate Event Managers Using Slack To Orchestrate Day-Of Venue Tasks Across Multi-Department Teams.

FAQ

What is the purpose of this AGENTS.md Template for order management architecture?

The template defines a concrete operating manual for a multi-agent order lifecycle, including inventory, payments, fulfillment, and notifications, with governance and handoffs.

Which agents are typically involved in this workflow?

Planner, InventoryAgent, PaymentAgent, FulfillmentAgent, NotifierAgent, ReconcilerAgent, AuditorAgent, and optional Domain Specialist agents for complex orders.

How are handoffs and decisions governed in multi-agent orchestration?

Handoffs occur via orchestrator with explicit state sharing and versioned context; agents must wait for orchestrator signals before progressing.

What are the security and data handling rules for this template?

Secrets are vault-based; access is role-limited; source-of-truth systems are authoritative; audit logs are mandatory.

How can I validate and deploy changes to an order-management AGENTS.md workflow?

Run unit and integration tests, perform canary deployments, and require manual review for high-risk changes to payments or stock state.