In this video I break down 5 agentic AI design patterns I keep coming back to when I’m building real GenAI systems with Microsoft AutoGen. The core idea is simple: most “single prompt = magic” agent setups fail in predictable ways—misalignment with the objective, messy decision-making, and sometimes straight-up infinite loops. So instead of guessing, I show you the patterns that make agents more reliable: Reflection (self-critique + iteration), Tool Use (calling APIs/DBs for grounded answers), ReAct (reasoning + acting in a tight loop), Planning (decompose big tasks into sub-tasks), and Multi-Agent (specialized agents collaborating). I also connect each pattern to what it fixes in production: Reflection improves quality control, Tool Use reduces hallucinations by fetching real data, ReAct makes the agent responsive, Planning stabilizes long workflows, and Multi-Agent helps you scale complexity without turning one agent into a confused generalist. If you’re building with Python and AutoGen (or porting the same ideas to other stacks), this is a practical system-design lens you can apply fast. Source code is linked, so you can clone and extend the projects instead of starting from scratch.