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agent-management

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Manage fleets of local AI agents: lifecycle, monitoring, task assignment, and resource control. Use when running multiple CLI agents via an orchestrator like AI Maestro.

skills/agent-management/SKILL.md

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# Agent Management

## Overview

Running one AI agent is a conversation. Running many is operations: lifecycle (start/stop/restart), health monitoring, task assignment, and resource limits.

The AI Maestro pattern: local CLI agents run in managed sessions (e.g., tmux), supervised by an orchestrator that assigns work and watches health.

Good management means: agents are visible, tasks are tracked, failures are noticed, and resources stay bounded.

## When to use

- Running multiple AI agents simultaneously
- Agents dying silently or hanging unnoticed