AGENTS.md Template for metrics and alerting design

Overview

Direct answer: This AGENTS.md Template for metrics and alerting design defines the operating context, roles, and rules for AI coding agents tasked with collecting, validating, and alerting on system metrics. It covers both a single agent and multi-agent orchestration patterns, including handoffs between agents and human review gates.

This template governs a workflow that ingests metrics data, evaluates thresholds, triggers alerts, and creates incident records when needed. It provides consistent memory and source-of-truth rules, tool governance, and strict validation checks to avoid context drift during agent handoffs.

When to Use This AGENTS.md Template

When designing an end-to-end metrics and alerting workflow powered by AI coding agents
When coordinating multiple agents for data collection, metric normalization, alert evaluation, and incident creation
When you need explicit rules for memory, sources of truth, and tool access in alerts pipelines
When enforcing security, deployment, and human review gates for production readiness

Copyable AGENTS.md Template

# AGENTS.md

Project role: Metrics and alerting orchestrator for AI coding agents
Agent roster and responsibilities:
 - Planner: designs data flow, thresholds, and alerting strategy
 - Implementer: builds metric extraction, normalization, and evaluation logic
 - Evaluator: validates outcomes, checks for drift, and approves escalations
 - Responder: actions on alerts, integration with incident systems
 - Researcher: sources domain knowledge and alert best practices
Supervisor or orchestrator behavior:
 - Maintains single source of truth for metrics data and alert state
 - Enforces handoffs to prevent duplicate work and context drift
 - Triggers human review when uncertain or high-severity incidents
Handoff rules between agents:
 - Planner to Implementer: provide data schemas, thresholds, and acceptance criteria
 - Implementer to Evaluator: share results, test outcomes, and drift checks
 - Evaluator to Responder: pass confirmed alerts and remediation steps
Context, memory, and source-of-truth rules:
 - Context must be refreshed at each handoff; avoid stale memory
 - All metrics sources and alert rules must be versioned in a central repo
 - Source of truth is the canonical metrics pipeline and incident system
Tool access and permission rules:
 - Agents may read data sources and write to approved endpoints only
 - Secrets must be accessed via a central vault; avoid hard-coded keys
 - Production systems require approval gates and audit trails
Architecture rules:
 - Use a modular, pluggable pipeline with clear interfaces
 - Separate concerns for data extraction, normalization, evaluation, and alerting
File structure rules:
 - Keep a tidy project tree; only relevant folders are allowed
 - All config lives under configs/ and is versioned
Data, API, or integration rules when relevant:
 - Use stable data contracts; validate schema and drift regularly
 - All API calls must be logged with trace IDs
 - Alerts must reference a single incident record
Validation rules:
 - Validation must run on every handoff and PR merge
 - Thresholds must be tested under synthetic loads
 - Drift checks must flag inconsistency within a defined tolerance
Security rules:
 - Secrets stored in vaults; no plain-text in repos
 - Access controlled by role-based permissions; all actions auditable
Testing rules:
 - Unit tests for each agent component; integration tests for end-to-end flow
 - Simulated incidents to verify alerting logic
Deployment rules:
 - Deploy to staging with feature flags; merge to production after approvals
 - Rollback plan for each deployment
Human review and escalation rules:
 - Escalate to humans for high-severity incidents or uncertain outcomes
 - All changes require sign-off from domain experts
Failure handling and rollback rules:
 - If an alert misfires or misses a true incident, rollback to previous stable state
 - Maintain a rollback checklist and audit the failure
Things Agents must not do:
 - Do not bypass approvals or introduce production changes without sign-off
 - Do not mutate production data without a test run and review
 - Do not rely on stale context for critical decisions

Recommended Agent Operating Model

This model assigns clear roles for planning, implementing, evaluating, and acting on alerts. The planner defines the design, the implementer builds the pipeline, the evaluator validates results, and the responder triggers actions. Handoff rules ensure clean transitions and minimize context drift. Human review gates protect production safety.

Recommended Project Structure

metrics-alerting/
├── configs/
├── data/
│   └── sources/
├── dashboards/
├── incidents/
├── agents/
│   ├── planner/
│   ├── implementer/
│   ├── evaluator/
│   ├── responder/
│   └── researcher/
├── tests/
├── pipelines/
└── docs/

Core Operating Principles

Explicit roles and responsibilities with clear decision boundaries
Single source of truth and versioned data contracts
Deterministic behavior with defined escalation paths
Guardrails for security, deployment, and human review

Agent Handoff and Collaboration Rules

Planner to Implementer: pass data schema, thresholds, acceptance criteria
Implementer to Evaluator: share results, test outcomes, drift checks
Evaluator to Responder: supply confirmed alerts and remediation steps
Researcher to any agent: supply domain knowledge and reference data

Tool Governance and Permission Rules

Command execution restricted to approved tools and endpoints
Config and secrets managed via vaults with strict RBAC
All API calls and actions are auditable and traceable
Production changes require explicit approvals and rollback plans

Code Construction Rules

Write modular, testable components; avoid global state
Keep interfaces stable and contract-based for data and events
Validate all inputs and outputs at each stage

Security and Production Rules

Zero trust: verify every access; short-lived credentials
Secrets in vaults; avoid hard-coded values
Sandboxed test environments; production only after approvals

Testing Checklist

Unit tests for all agents and components
End-to-end integration tests for the metrics and alerting flow
Performance tests with synthetic incidents
Deployment tests with feature flags and rollbacks

Common Mistakes to Avoid

Skipping human review for high-severity alerts
Allowing drift in metric contracts between components
Overloading the planner with too many responsibilities

FAQ

What is the purpose of this AGENTS.md Template for metrics and alerting design?

This template provides operating context, roles, and rules for building AI coding agents that collect metrics, evaluate thresholds, and manage alerts and incidents.

How many agents are typically involved in this workflow?

At minimum, planner, implementer, evaluator, and responder with optional researcher for domain knowledge.

How are handoffs between agents handled?

Handoffs pass data schemas, results, and acceptance criteria; memory is refreshed at each transition to avoid drift.

What ensures the security and production readiness of this workflow?

Secrets in vaults, RBAC, audit trails, approvals, and explicit rollback plans before production changes.

How is testing performed for the metrics and alerting template?

Unit tests, integration tests, synthetic incidents, and deployment tests with feature flags and monitored rollbacks.

Target User

Use Cases