How to Build a Docs Update Agent With Next.js

A docs update agent starts from a clear trigger and ends with a handoff a person can check. The steps below cover that path, and the code shape after them lists the fields one run needs.

1. 1Define the trigger: issue, PR, failed CI job, docs change or support ticket.
2. 2Limit tool access to the files and systems the agent needs.
3. 3Add a plan step before writes.
4. 4Run checks and produce a short reviewer handoff.
5. 5Log the prompt, changed files, commands and result.

type AgentJob = {
  task: string;
  context: string[];
  allowedTools: string[];
  checks: string[];
  handoff: "diff" | "comment" | "pull_request";
};

Decide allowedTools first. While you build, broad access is tempting, because narrowing it is fiddly and everything works when you are watching. An agent that runs unattended with write access to the whole repo and a shell is a different kind of risk from one that can edit three paths and run a single command. You find out which one you built on the run where something goes wrong.

handoff decides whether people will trust the agent. For an agent that changes code, returning a diff keeps a person in front of every change before it lands, which is the right default while you learn how the agent behaves on your codebase. A pull request keeps the same gate with less friction once you have more confidence in the agent.

A comment is the lightest handoff and suits agents that only diagnose. Whichever you choose, don't let the agent merge until the commands in checks are ones you would have run yourself.

Most failures here are ordinary. The agent gets too much room, or someone trusts a "done" that no test proved.

Scope problems show up first. Give an agent a vague task and it will find related work to do, which looks like initiative until you face a diff across twenty files and can't tell which changes were the point. Narrow the task instead: name the files, name the behavior that should change and say what is out of bounds. A stronger model won't solve a scope problem.

Weak checks show up last and do the most damage. An agent reports success from its own reading of its own work, and nothing outside it says otherwise. If the agent's summary is the only thing between its work and your main branch, you have no review step. Give it a command whose exit code you trust, and fail the run when that command fails.

Attach a trigger to a cloud agent with Automations, so it runs on its own. Automations can start from pull request events such as opened, merged and draft, from messages in a public Slack channel, and from Linear, PagerDuty, Sentry, webhooks or a schedule. For docs, the merged trigger fits best: on every merge, a cloud agent reads the changed code and updates the docs. Automations aren't included on Free, Plus or the legacy Start plan.

Yes. With Cursor connected to Slack, the whole loop runs in one thread with a person in it. In one case, there were no docs for an AWS Bedrock integration. The PM raised that in Slack, and Cursor read the code and wrote the docs. An engineer replied in the thread to flag two errors, Cursor fixed them from the same thread, and the update shipped within 15 minutes, entirely over Slack.

The team that hears from customers first. On the old path, support finds a gap in the docs and files a ticket, and a technical writer gets to it eventually. That handoff over the fence stalls the feedback loop. With the docs open in their own Cursor workspace, support can check whether something is documented, make the change, render the docs site to review it and open a pull request from the same customer conversation. A speaker in Cursor's technical support webinar called docs work low-risk, high-agency and high-impact for support.

Merged, for docs. Opened is tempting, because the docs draft would be ready at the same time as the code review. But a pull request under review is still changing, so docs written against its first version can describe behavior the reviewers talked the author out of.

The wasted run is the small cost. The larger one is a draft that reads as authoritative, sitting in the review channel next to correct ones, with nothing on it to say which version of the code it describes. A merged pull request is settled. Waiting for the merge gives you one draft instead of a queue of drafts to re-check.

That the site still builds and that whatever link checking you run still passes. No check proves that a sentence in the docs is true.

This is where the advice above, to give the agent a command whose exit code you trust, runs out. Prose has no exit code for accuracy, so the check has to be a person. The design problem is making that person's review cheap.

The Slack loop above shows one way to do that. The engineer who read the draft in the thread was the check, and flagging the two errors took one reply in the same thread.

Because "low-risk" describes who should be allowed to edit the docs, not whether anyone reads a change before it ships. Letting support open doc pull requests from a customer conversation costs little, and someone still reviews each one. Letting an agent publish unread is a different bet. We think a confidently wrong page costs more support time than the gap it filled, because readers act on what the docs say. The recipe above already includes the cheap safeguard: the agent posts its result to a private Slack channel, and a person reads it there before it ships.