I’ve been doing QA automation for over 20 years. I started at IBM, moved to many organizations like Infosys, ANZ, NAB, Capgemini, and I watched the same thing happen over and over, teams spend weeks writing test cases by hand, running them manually, triaging bugs in spreadsheets. It’s tedious. It doesn’t scale. With AI, it doesn’t have to be that way. This course teaches you how to build AI systems that do that work for you. Real systems. Production-ready systems using the Microsoft Agent Framework that you can deploy and use.
What You’ll Build
Two hands-on projects:
- Project 1: A Bug/Defect Triage Agentic AI Solution: Build an AI system that reads your Jira tickets, understands what the bug is, figures out how serious it is, assigns it. Learns from the decisions you make and gets better.
- Project 2: Test Analysis to Automation Script Creation Agentic AI Pipeline: User story comes in → agent reads it → generates edge cases → writes Playwright tests. The whole flow. You’ll learn how to get multiple AI agents working together instead of against each other.
Why This Actually Works
The hard part about agentic AI isn’t the AI part. It’s getting multiple agents to coordinate. I spent months debugging race conditions in the Microsoft Agent Framework, figuring out why GroupChat was failing, solving orchestration problems. That’s what this course teaches, the real stuff that makes it work. You learn the foundations fast (what are LLMs, how do agents work). Then you build. Jira integration, Playwright, all woven together with Python. You also see where things go wrong and how to fix them. That’s the difference between a tutorial that leaves you hanging and one that actually prepares you.
