AI agents don’t fail because of bad models—they fail because they follow the wrong patterns. Behind every hallucinated action or erratic tool call is an invisible structure: how agents retrieve, process, and decide in real time.

This talk pulls back the curtain on agent behavior patterns—the hidden loops, stale memories, and misaligned retrievals that quietly sabotage outcomes. You’ll learn:

* The 3 most common failure patterns in agentic AI—and why they’re hard to catch

* How agents lose context and fall into rigid loops (even in well-orchestrated chains)

* A blueprint for building adaptive agents using real-time behavioral context

You’ll leave with practical tools for making agents more reliable, interpretable, and self-correcting—without overhauling your stack.