What is context engineering?

Context engineering is deciding what an AI agent should know before it starts work, and making sure that information is accurate, current and easy to retrieve.

Key takeaways

• Most agent mistakes come from missing context, not from a weak prompt.
• Good context answers three questions: what to do, why, and where in the system.
• Context that lives next to the work stays fresher than context copied into prompts.
• You can improve context with habits, without new tools.

Prompting versus context

A prompt is one message. Context is everything around it: the architecture docs, the API contract, the ticket, the decision that ruled out the obvious approach last quarter. An agent with a perfect prompt and no context will still write code that ignores your conventions.

That is why teams that get good results from agents spend their effort on the inputs. The instructions can be short when the surrounding information is right.

The three questions

Every task should carry answers to these before an agent, or a new hire, picks it up:

• What needs to change, in plain language.
• Why it matters, so trade-offs can be made sensibly.
• Where in the system: which services, endpoints and docs are involved.

If a task cannot answer these, the work will start with a meeting. The same is true for agents, except they will not ask for the meeting. They will guess.

Practical habits

1. Write the why on every task. One sentence is enough.
2. Add acceptance criteria. A checklist gives the agent, and the reviewer, a shared definition of done.
3. Link, do not paste. Attach the relevant doc or endpoint to the task instead of copying text, so the link stays current.
4. Record decisions. A short note on why an option was rejected prevents the next person from retrying it.
5. Keep docs close to the work. A doc attached to a task or feature is found and corrected far more often than one in a separate wiki.

Where tooling helps

Osco is built around this idea. Tasks carry their reason, acceptance criteria and dependencies. Docs, API endpoints and features are linked to the tasks they explain, and an MCP server lets a coding agent read all of it with your permissions. The result is that "what, why and where" travels with the task.

Conclusion

Context engineering is less about clever prompts and more about disciplined, connected documentation. Start by making every task answer what, why and where, and the quality of both human and agent work improves.