What problem does it solve? Running AI agents as continuously available services introduces operational risks that single CLI sessions never face: uncontrolled restarts, missing audit trails, silent failures, and runaway costs. This Skill provides the operational controls needed to run cloud-hosted or always-on agent systems safely. ## Core Features & Use Cases - Lifecycle Management: Standardized procedures to start, pause, stop, and restart agent runtimes across PM2, systemd, and container orchestrators. - Observability & Metrics: Track success rate, retries per task, time to recovery, cost per successful task, and failure class distribution. - Safety Controls: Enforce least-privilege credentials, secret injection, kill switches, timeout budgets, and audit logs for high-risk actions. - Use Case: When failure rates spike on a production agent deployment, follow the incident pattern: freeze rollouts, capture representative traces, isolate the failing route, patch with the smallest safe change, run regression and security checks, then resume gradually. ## Quick Start Use the enterprise-agent-ops skill to define lifecycle controls, metrics, and an incident response plan for my continuously running agent deployment.
