What problem does it solve? Running AI agents as continuously hosted services introduces operational risks that single CLI sessions never face: uncontrolled rollouts, missing audit trails, runaway costs, and failures with no recovery path. This Skill provides the operational controls needed to run agent workloads safely in production. ## Core Features & Use Cases - Lifecycle Management: Standardized procedures to start, pause, stop, and restart long-running agent processes across PM2, systemd, and container orchestrators. - Observability & Metrics: Track success rate, retries per task, time to recovery, cost per task, and failure class distribution through logs, metrics, and traces. - Safety Controls: Enforce least-privilege credentials, environment-level secret injection, hard timeouts, retry budgets, and audit logs for high-risk actions. - Use Case: When failure rates spike on a deployed agent fleet, follow the incident pattern: freeze rollouts, capture representative traces, isolate the failing route, apply the smallest safe patch, run regression and security checks, then resume gradually. ## Quick Start Use the enterprise-agent-ops skill to define lifecycle controls, observability metrics, and an incident response plan for my continuously running agent deployment.
