Day 24: Agentic AI in DevOps & MLOps ⚙️🚀

Executive Summary DevOps and MLOps are where agentic AI becomes real infrastructure. Unlike research or product agents, DevOps/MLOps agents: touch production systems 🧨 trigger deployments modify infrastructure influence reliability, cost, and security This makes them: extremely powerful extremely dangerous if designed poorly This chapter goes deep into: where agentic AI fits in DevOps & MLOps safe architectural patterns concrete implementations observability, analytics, and cost controls real-world use cases and failure modes Think of these agents as junior SREs that never sleep — and must never panic. Why DevOps & MLOps Are Agent-Native Domains 🧠 DevOps/MLOps work is: procedural reactive signal-driven repetitive under pressure Classic loop: Observe → Diagnose → Decide → Act → Verify This maps perfectly to agentic systems. But mistakes here cause: outages data corruption massive cloud bills 💸 So autonomy must be earned, not assumed. What DevOps Agents SHOULD and SHOULD NOT Do 🚦 SHOULD