Agentic DevOps: Build Autonomous AI Agent and AI Agents team

About this course

Traditional DevOps automation follows rigid, pre-written rules — if this happens, run that script. Agentic DevOps is the next evolution: autonomous agents that perceive a system failure, reason through real logs and real commit history, investigate the actual root cause, and — when confidence is high enough — execute a safe, governed fix themselves, escalating to a human only when it isn't.

This is a hands-on, technical masterclass built around one core idea: you will build these agents yourself, from real Python code, using CrewAI and the OpenAI and GitHub APIs directly. Nothing here is a black box you prompt and hope. By the end, you will understand exactly how your agents reason, decide, and act — because you wrote every line that makes them do it.

Before you write a single line of an agent's own code, you'll spend real time on the layers most agentic AI content skips entirely: AI Agent Architecture and Agentic AI Governance. You'll study the actual design patterns — the agentic loop, orchestrator/worker coordination, and the emerging protocols, such as A2A and MCP (Model Context Protocol), that are standardizing how agents talk to tools and to each other. These are the patterns that separate an agent a company can actually trust with production access from one that gets quietly shut down.

Gartner already predicts that over 40% of agentic AI projects will be cancelled by 2027, not because the models weren't smart enough, but because of exactly this: unclear architecture, ungoverned risk, and costs nobody designed for. This course is built to prevent this. A dedicated section on AI Agent Security covers critical vulnerabilities like prompt injection and tool poisoning, providing a complete Agentic AI Governance framework. This framework includes context engineering, agent identity and authorization, and the essential "kill switches" that enterprise security teams demand.<

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