AI agent development is evolving from simple chatbots to complex, autonomous systems capable of handling intricate tasks like automating workflows and managing codebases. However, a significant challenge emerges with the increasing amount of context these agents need to track, such as chat history and tool outputs. The traditional approach of expanding context windows in models has limitations; simply adding more tokens makes agents slower and more costly while risking signal degradation and physical constraints. To tackle these issues, the Google Agent Development Kit (ADK) introduces "context engineering," which treats context as a structured system with its own architecture and life cycle. Instead of viewing context as a simple, mutable string, ADK represents it as a compiled view of a richer, stateful system. This involves separating storage from presentation, creating explicit transformations, and limiting the context scope for each agent call. The architecture consists of distinct layers: working context, session logs, long-term memory, and artifacts. ADK employs a flow of processors that compile context for each invocation, allowing for a flexible and efficient transformation of state into actionable context. This structured approach results in model agnosticism, rich operations for debugging and memory management, and increased observability through detailed event records. The system captures every interaction as strongly-typed events, which enhances not only the management of context but also the robustness of decision-making across different agents.