• views: 146
• date: 5 February 2026
• posted by: 0nelove

Learn how to detect, diagnose, and recover from AI agent failures when security guardrails fail using proven reliability patterns and hands‑on workflows that support safe, scalable production systems.
Course details
As AI agents shift from experimentation to production, operational failures can create serious business risks. This intermediate course explores practical techniques for monitoring agent behavior, tracing execution paths, and identifying failure modes across single‑ and multi‑agent systems. Through hands-on GitHub Codespaces exercises, you learn how to implement rollback mechanisms, build automated recovery workflows, and create reports that surface agent health and system status in real time. By the end of the course, you'll have the skills to improve the safety and predictability of AI agents in production, and to respond quickly and effectively when failures occur.
Skills covered
AI Security, AI Policy, Governance, and Regulation, Agentic AI Development





   
   
   

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