What is Context Engineering?

How context engineering works

At inference time, a language model can draw on several layers of information:

• System instructions: behavioral guidelines, role definitions, constraints.
• Conversation history: prior turns in the current session.
• Retrieved knowledge: external documents, database results, API responses.
• Long-term memory: information persisted across sessions.
• Tool definitions and outputs: descriptions of functions the model can invoke, and what they return.
• Structured outputs: format specifications for responses.

Context engineering is the practice of designing all of this deliberately, rather than letting it accumulate by default. In practical terms, that means building retrieval pipelines, shaping tool outputs, scoping access per task, compressing history, and verifying that what reaches the model is accurate, current, and relevant to the next step.

Why it matters

According to LangChain’s 2025 State of Agent Engineering report, 57% of organizations now have AI agents in production. The top-cited barrier is output quality. Rephrasing prompts rarely moves the needle on these failures because the problem is upstream: the model doesn’t have the right information at the right time.

Most agent failures trace back to one of three context problems:

• Missing information. The model answers confidently without knowing something relevant. It doesn’t ask because it doesn’t know it doesn’t know.
• Stale information. The model draws on data that was once accurate but is no longer. A customer support agent with last year’s pricing will confidently give wrong answers.
• Overloaded information. The model has too much context to reason over. Chroma’s context rot research shows accuracy drops from 95% to 60-70% as input length grows on trivially simple tasks.

These are engineering problems, not prompting problems. They require designing systems around the model, not just better sentences.

Common misconceptions about context engineering

• “It’s just prompt engineering with a new name.” Prompt engineering is one layer inside context engineering. The other layers (retrieval, memory, tools, structure, compression) sit outside the prompt and usually matter more.
• “Bigger context windows make context engineering obsolete.” Larger windows raise the ceiling on what fits, not on what the model attends to. Accuracy still degrades at long input lengths, and cost scales with every token you include.
• “It’s the same as RAG.” RAG is the retrieval slice. Context engineering also covers what the model sees before retrieval, how tool outputs are shaped, how memory is pruned, and how scope is enforced per user or task.
• “Demos prove context engineering doesn’t matter.” Demos work because the developer hand-crafts the context. Production fails because the same care isn’t applied to every query.

Context engineering and Wire

Wire handles the retrieval and storage slices of context engineering. Files land in a container, get chunked and indexed, and are exposed through five MCP tools (wire_explore, wire_search, wire_navigate, wire_write, wire_delete). Your agent decides when to call which tool; Wire returns scoped, typed results instead of raw document dumps. That leaves you to design the layers Wire doesn’t own: system instructions, orchestration, and what to do with the results.