Agentic DevOps: Build Autonomous AI Agent and AI Agents team

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.

What You Will Build:

• A self-reviewing CI/CD pipeline where an AI agent gates every pull request, blocking merges that contain real security risks — enforced by GitHub itself, not just left as a comment

• An Incident Autopilot that investigates production alerts by correlating a live GitHub Issue, real commit history, and infrastructure changes, then posts its findings back to the same ticket

• A full multi-agent “AI DevOps Workforce” built with CrewAI: an Investigator, a Communicator, and a Commander agent, handing work to each other in sequence, exactly the way a real incident response team would

• An Auto-Remediation Agent that acts on its own confidence score — executing a governed rollback automatically, or requesting human approval when the stakes are too high to act alone

What Makes This Course Different:

• Real, working code against real APIs, at every single step — no vendor-locked tool, no clicking through someone else’s built-in AI feature

• Genuine multi-agent orchestration with CrewAI — agents that hand off real work to each other, with a visible, auditable trail of who decided what

• A complete governance layer most agentic AI content skips entirely: context engineering, agent identity and authorization, kill switches, and a practical framework for deciding exactly where to deploy your first agent, safely, inside a pipeline that already exists

• Every lab is designed to translate directly from your laptop to a real engineering organization — the same patterns, prioritization thinking, and governance discipline scale from a solo project to a whole team’s pipeline, without anything here depending on a specific paid platform

• No hidden costs to worry about — every lab runs on a free GitHub account and a few cents of API usage

Course Objectives:

• Distinguish agentic workflows from both traditional automation and simple AI-assisted coding

• Design and orchestrate multi-agent systems with clear roles, handoffs, and shared context

• Implement governance patterns — kill switches, confidence-based escalation, and audit trails — that keep autonomous agents accountable

• Evaluate, in a real pipeline, exactly where an AI agent should go first, and why

What You’ll Be Able to Do After This Course:

• Architect and build agentic systems that reason, decide, and act — not just chat

• Lead an agentic DevOps initiative anywhere you work, with a real, defensible prioritization framework instead of guesswork

• Speak with authority about multi-agent orchestration, agent governance, and production-grade AI operations in any technical conversation or interview

And once you’ve mastered the foundations, a new advanced section takes you even further: GitHub Agentic Workflows, exploring how GitHub itself is becoming a native AI platform — from natural-language pipelines to fully agentic CI/CD.

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