This article addresses the critical bottleneck of context management in scaling sophisticated AI agents for long-horizon tasks, arguing against merely increasing context window size. It proposes "context engineering" as a new discipline, treating context as a first-class system with its own architecture and lifecycle. Google's Agent Development Kit (ADK) is introduced as an open-source, multi-agent-native framework built on the thesis that context is a "compiled view" over a richer stateful system. ADK's architecture adheres to three core principles: separating storage from presentation, explicit transformations via named processors, and default context scoping. The framework details a tiered context model comprising working context, session (a durable log of structured events), memory (long-lived, searchable knowledge), and artifacts (large external data). It emphasizes pipeline-based context processing, compaction, caching, and agent-directed relevance management for artifacts and memory. For multi-agent systems, ADK provides explicit scoping mechanisms and conversation translation to prevent context explosion and maintain agent coherence during handoffs, moving beyond ad-hoc string manipulation to a rigorous systems engineering approach for production AI agents.
