Mastering AI Agents with AutoGen & CrewAI

About this course

This course contains the use of artificial intelligence. AI tools were used only to rephrase and fix grammar in the course description and supporting text. AI agents are crossing from demos into production, and the gap between watching one and building one that you'd trust on a schedule is exactly what this course closes. You will build real, working agents and multi-agent systems with the two frameworks the industry has settled on — CrewAI for role-and-task crews, and AutoGen for conversation-driven agents — and learn the model that underlies both, so you're never locked into one tool.

This is a hands-on, build-along course for software engineers, data scientists, solution architects, and technical analysts who want to ship agents, not just talk about them. No prior agent experience is assumed — you start from the agent loop itself — but you should be comfortable writing basic Python. By the end you will have built four projects: a research crew, a multi-agent support system, a code-running data-analysis agent, and a deployed, observable multi-agent service.

We go well past 'hello agent'. You'll give agents real tools, databases, memory, and retrieval over your own documents; then you'll do what most tutorials skip — make them reliable. You'll diagnose why agents loop, drift, and invent tool calls, add guardrails and cost caps, and actually evaluate whether an agent worked. Finally you'll deploy one: behind an API, with secrets handled, observability on, and a schedule firing it automatically.

Every concept is taught from the operations floor, with both dollar and rupee cost examples, by Ganesh Ravikumar, who has spent 20+ years putting automation — and now agents — into real, regulated operations where a runaway loop or a surprise bill is a genuine problem. This is the course I wish existed when I shipped my first agent into production. Enroll now and build agents that actually do the work.

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