AGENTS.md Template: Queue-Based Autoscaling

Overview

This AGENTS.md template defines a queue based autoscaling workflow for AI coding agents. It supports single agent operation and multi-agent orchestration in a queue driven environment, with clear handoffs, tool governance, and human review hooks. Direct answer: it codifies the roles, rules, and interactions needed to scale resources based on queue metrics while preserving safety and auditability.

When to Use This AGENTS.md Template

• When you operate a queue driven system that must scale compute resources automatically.
• When you require explicit handoffs between planner, autoscaler, and health/resource managers.
• When governance, auditing, and human review are required for production changes.
• When you want a single source of truth for agent interactions and decisions.

Copyable AGENTS.md Template

# AGENTS.md
Project role: Queue-based autoscaling orchestrator for cloud resources in a queue driven environment.

Agent roster and responsibilities:
- Planner: Monitors queue depth, SLA, and policy; decides scaling actions and produces a plan.
- Autoscaler: Executes scaling actions against cloud platforms; ensures idempotent operations and rollback.
- HealthMonitor: Tracks queue health, worker health, and resource budgets; flags anomalies.
- ResourceManager: Provisions or releases resources; ensures proper tagging and accounting.
- DataFetcher: Gathers metrics from monitoring endpoints; updates memory.

Supervisor or orchestrator behavior:
- Orchestrator coordinates all agents, enforces timeouts, and routes plans to execution agents; logs decisions and escalates when thresholds breached.

Handoff rules between agents:
- Planner > Autoscaler for execution
- Autoscaler > HealthMonitor after action
- HealthMonitor > Planner if re evaluation is needed
- DataFetcher > Planner/Autoscaler to update context as needed

Context, memory, and source-of-truth rules:
- All state stored in a central memory store; source of truth is the system state store and execution logs; memory entries include a timestamp and policy version.

Tool access and permission rules:
- Agents may call cloud APIs with restricted permissions; secrets stored securely; no hard coded credentials; actions require appropriate approvals for sensitive ops.

Architecture rules:
- Event driven, idempotent, auditable; avoid side effects without confirmation.

File structure rules:
- Place this AGENTS.md at project root as the single source of truth for this workflow.

Data, API, or integration rules:
- Use official APIs; validate schemas; respect rate limits; log all external calls.

Validation rules:
- Pre checks before scaling; post checks after actions; verify invariants.

Security rules:
- Encrypt secrets; restrict network egress; monitor for breaches.

Testing rules:
- Unit tests for decision logic; integration tests for actions; end to end tests for production like scenarios.

Deployment rules:
- Canaries for production changes; rollback on failure or degraded health.

Human review and escalation rules:
- On SLA breach or uncertain decisions, escalate to on call engineer; manual override allowed with audit.

Failure handling and rollback rules:
- If action fails, revert to previous state; record rollback reason; alert operators.

Things Agents must not do:
- Do not scale beyond max or below min; do not perform destructive actions without checks; do not skip validation.

Recommended Agent Operating Model

The default operating model is a two-layer approach: a Planner that decides when to scale and what actions to take, and an Autoscaler that performs the actions. In a multi-agent setup, an Orchestrator coordinates Planner, Autoscaler, HealthMonitor, and DataFetcher to ensure alignment with policy and SLAs. Decision boundaries: planner sets scaling thresholds; escalation paths trigger human review for ambiguous or high-risk changes.

Recommended Project Structure

ai-workflows/queue-autoscaling/
  orchestrator/
  planner/
  autoscaler/
  monitors/
  memory/
  policies/
  configs/
  tests/

Core Operating Principles

• Clear ownership and accountability for each agent role.
• Idempotent actions and auditable decision logs.
• Single source of truth for context and state.
• Least privilege for tool access and secrets management.
• Observability through metrics, traces, and robust tests.
• Safe escalation paths and human review when needed.

Agent Handoff and Collaboration Rules

• Planner hands off to Autoscaler with a具体 plan and time to execute.
• Autoscaler reports outcomes to HealthMonitor and Memory.
• HealthMonitor triggers Planner re-evaluation if SLA or health metrics drift.
• DataFetcher updates context and can trigger Planner re-planning.
• Domain specialists can annotate decisions via the Orchestrator with approval gates.

Tool Governance and Permission Rules

• Actions to cloud resources require least privilege permissions and approval gates for production changes.
• Secrets must reside in a secret store; no plaintext credentials in logs.
• API calls are rate-limited and auditable; all external calls are logged.
• Production changes require canary deployment and rollback paths.

Code Construction Rules

• Idempotent scaling actions with deterministic outcomes.
• Validate inputs against schemas before acting.
• All decisions versioned; config changes require review.
• Use feature flags to enable incremental rollout.
• Logging includes context ids and timestamps for traceability.

Security and Production Rules

• Encrypt all secrets in transit and at rest.
• Limit network access to required endpoints only.
• Implement monitoring and alerting for failed actions and anomalies.
• Require manual approval for risky production changes.

Testing Checklist

• Unit tests for Planner decision logic and Autoscaler actions.
• Integration tests that simulate queue depth changes and scaling events.
• End to end tests for a full run with health checks and rollbacks.
• Security tests for secret handling and access controls.

Common Mistakes to Avoid

• Overlapping scaling thresholds that cause oscillations.
• Missing audit trails for decisions and actions.
• Unauthorized access to production resources or secrets.
• Silent failures due to partial retries without rollback.

Related implementation resources: AI Use Case for Content Marketers Using Wordpress To Auto-Translate Blog Posts Into Multiple Languages 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?

To codify a queue based autoscaling workflow for AI coding agents, enabling single-agent operation or multi-agent orchestration with clear handoffs, rules, and governance.

How do agents hand off tasks in this workflow?

Planner proposes a plan; Autoscaler executes; HealthMonitor validates; DataFetcher updates context; Handoffs are explicit and logged.

Where is the context stored and how is the truth maintained?

All context resides in a central memory store and a system state store with a single source of truth; updates are timestamped and auditable.

What should developers do to customize thresholds?

Adjust policy thresholds in the Planner rules and memory policies; validate changes in a staging environment before production.

What are the security and deployment constraints?

Encrypt secrets, restrict network access, and use controlled deployment with canary checks and rollback.