AI agents don’t think in isolation—they follow patterns of behavior based on the context they retrieve, the decisions they make, and the data they rely on. Yet most AI today is blind to its own patterns, reacting without understanding the broader structures influencing its actions.

This workshop will uncover the emergent patterns that shape agentic AI and how we can design agents that leverage real-time context to become more adaptive, reliable, and effective. We’ll explore:

Common behavioral patterns in AI agents—how they retrieve, process, and act on context.
Why agentic AI needs structured, evolving context to break free from rigid, static behavior.
How to analyze, refine, and optimize agentic patterns for better decision-making and adaptability.
Through hands-on exercises, participants will learn how to trace, visualize, and shape AI agent behavior using dynamic context retrieval, interaction modeling, and live data adaptation.

If we want AI agents to be truly intelligent, we need to understand the patterns they follow, the context they need, and the behaviors they evolve—this workshop will teach you how.