What is Context Engineering

Context engineering extends prompt engineering by focusing on the entire set of information - system instructions, tool definitions, message history, external data, and more - that enters a language model’s limited attention window to drive desired behavior. While prompt engineering crafts the words and examples given directly to a model, context engineering curates which tokens and resources should be present at each inference step, balancing relevance against the model’s finite “attention budget.”

An effective context engineering strategy begins with minimal, high-signal system prompts that provide clear, concise guidance without overloading the model. Instead of embedding brittle, step-by-step logic or exhaustive rules, system prompts should hit the Goldilocks “altitude”—specific enough to direct behavior yet flexible enough to leverage the model’s general reasoning capabilities. Structuring prompts into distinct sections (background, instructions, tool guidance, output format) further helps the model parse and prioritize information.

Tool definitions play a critical role in context engineering. Well-designed tools act as precise contracts between the agent and its environment, enabling efficient addition of new context only when needed. By limiting tool functionality to clearly scoped, non-overlapping actions and providing descriptive, unambiguous parameters, tools minimize token waste and reduce decision ambiguity. Curating a minimal, robust tool set prevents context bloat and streamlines agent reasoning over multiple inference turns.

For long-horizon or multi-turn tasks, context engineering employs techniques such as compaction, structured note-taking, and sub-agent architectures. Compaction distills past conversation or tool outputs into concise summaries, freeing up context space while preserving crucial information. Structured note-taking moves persistent state outside the model’s context window, reintroducing only relevant excerpts when necessary. Sub-agents with focused, narrow contexts perform detailed work in isolation and return condensed summaries for the primary agent to synthesize, preventing context overload.

Finally, dynamic context retrieval—loading information “just in time” via embedding-based search or on-demand tool calls—ensures agents have access to up-to-date, relevant data without preloading entire datasets. Hybrid strategies can preload static context and defer dynamic retrieval for volatile information, achieving a balance between speed and relevance. By treating context as a finite, evolving resource, context engineering enables the creation of more capable, maintainable AI agents that operate reliably across complex, extended tasks.