What is Context Engineering?
Context engineering is the discipline of deciding what information enters a model's limited context window at each step — and what stays out. As agents run over many steps, naively stuffing everything into context degrades quality and cost. Context engineering curates the right instructions, retrieved knowledge, tool results and memory so the model has exactly what it needs, when it needs it. It is a core part of harness engineering.
Definition
Context engineering is the practice of curating, compressing and sequencing the information placed in a model's context window so it has the most relevant signal — and the least noise — at each step.
Key takeaways
- Context is a scarce resource; what you leave out matters as much as what you include.
- More context is not better — irrelevant tokens degrade quality and raise cost.
- Techniques: retrieval, summarization, compaction, and structured memory.
- It generalizes prompt engineering from one prompt to a whole agent run.
- It is a core layer of the harness around a model.
Context
Every model has a finite context window, and quality degrades when it is filled with low-signal content. In single-turn use this is manageable, but agents accumulate history, tool outputs and retrieved documents across many steps, quickly overwhelming the window.
Context engineering treats the window as a budget to be managed deliberately: keep the durable instructions, retrieve only what is relevant now, summarize or compact the rest, and store long-term state outside the window in memory.
Architecture
Core moves: select (retrieve only relevant passages), compress (summarize prior steps), compact (drop or fold stale turns), and externalize (push long-term state to a memory store, pulling it back on demand).
In an agent loop, context is reassembled each step from layered sources: stable system instructions, task state, relevant retrieved knowledge, recent tool results and selected long-term memories — ordered so the most important signal is most salient.
Components
Benefits
- Keeps quality high as tasks grow long.
- Controls token cost and latency.
- Reduces distraction and hallucination from noise.
- Enables long-horizon agents within finite context.
Risks
- Over-aggressive compression can drop needed information.
- Poor retrieval injects irrelevant or wrong context.
- Complexity in deciding what to keep each step.
- Bugs here surface as subtle quality regressions.
Tools & technologies
Examples
- Summarizing earlier agent steps so the window stays focused on the current subtask.
- Retrieving only the policy section relevant to a question instead of the whole manual.
- Storing a user's preferences in memory and recalling them only when relevant.
FAQs
- How is context engineering different from prompt engineering?
- Prompt engineering crafts a single instruction. Context engineering manages the full set of information in the window across an entire agent run — retrieval, memory, tool results and compression included.
- Why not just use a bigger context window?
- Larger windows help but do not eliminate the problem: quality and cost degrade as windows fill with low-signal tokens. Curation still wins.
- How does it relate to RAG and memory?
- RAG and memory are sources of context; context engineering decides what from them actually enters the window, when, and in what form.
- Is it part of harness engineering?
- Yes. Context management is one of the core layers of the harness that turns model capability into reliable agent behavior.