Context engineering in ever changing environment - made simple

As AI systems evolve from simple chat-based interactions to complex, autonomous agents, one fundamental challenge remains unsolved: keeping their context accurate and up to date in a world that never stops changing. Enterprises sit on massive, dynamic datasets — documents, codebases, API. Yet most data infrastructures are batch-oriented and blind to change, forcing engineers to rebuild from scratch every time data or logic shifts. This breaks the feedback loop between real-time world state and AI decision-making. In this talk, we’ll explore how context engineering — the process of maintaining AI’s “mental model” of the world — can be made simple and reliable using CocoIndex, a Python-native data transformation engine designed for AI workloads. We’ll walk through how CocoIndex builds a real-time knowledge graph in Neo4j that continuously reflects code and document changes — enabling AI agents to reason, retrieve, and act with always-fresh context.