An AI agent can have the best model, the right tools and a carefully written system prompt, yet still make the wrong decision because it was given the wrong context.

As agents become more capable, context is becoming an engineering problem of its own. Too little context and the agent misses critical information. Too much and the signal gets buried in noise. Stale context, conflicting instructions, irrelevant retrievals and poorly scoped tool results can all lead an agent toward confident but incorrect actions.

This session explores context engineering as a core discipline for building reliable AI agents. Through practical examples, we will examine how context flows through an agent, where it can become noisy or misleading, and how to design context deliberately across instructions, retrieved information, conversation state, memory and tool outputs.

We will compare different context strategies and examine the trade-offs between relevance, reliability, latency and cost. The goal is to move beyond prompt engineering and treat context as an explicit part of the agent architecture.

Attendees will leave with practical patterns for designing agents that receive the right information at the right time, instead of simply giving an increasingly large amount of information to an increasingly capable model.