Context Engineering
Context Engineering is a technical discipline focused on systematically designing and optimizing the context provided to AI models — including codebase structure, commit history, design intent, and domain knowledge.
If prompt engineering is the craft of "how to write a single question well," context engineering sits one layer above it. It is the work of designing "what to show the AI, in what order, and how much." Anthropic's 2026 report introduces a concept called "Repository Intelligence" — the ability of an AI agent to work with an understanding of the relationships and intent of an entire repository, rather than at the level of individual lines of code. Achieving this makes the quality and structure of the context passed to the agent critically important. Concretely, this involves design decisions such as the following: - Which files to include in the context, and which to exclude - How to communicate project rules (coding conventions, architectural policies) - How to narrow down past change history to the portions relevant to the current task - How to compress information to maximize its density within the constraints of the context window Claude Code's CLAUDE.md and Rules files, and OpenClaw's long-term memory feature, are all examples of context engineering in practice — mechanisms by which developers structure project-specific knowledge and pass it to the AI. The phase of refining how prompts are written is over. We have entered the phase of designing "the environment itself in which the AI works."
Related Terms
Prompt Engineering
Prompt engineering is the practice of designing the structure, phrasing, and context of input text (prompts) in order to elicit desired outputs from LLMs (Large Language Models).
MCP (Model Context Protocol)
MCP (Model Context Protocol) is a standard protocol that enables AI agents to connect to external tools, databases, and APIs. It is an open standard developed by Anthropic and donated to the Linux Foundation's Agentic AI Foundation.
Ambient AI
Ambient AI refers to an AI system that is seamlessly embedded in the user's environment, continuously monitoring sensor data and events to proactively take action without requiring explicit instructions.
