How do AI Employees keep your docs in sync with the code?
A docs AI Employee that reads the code merged since its last run, rewrites the pages those changes affected, and opens a single pull request for review. Nothing publishes without a named human's merge.
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Documentation falls behind the code. The README, the setup guide, the API reference, and the architecture notes drift a release or two back while the code keeps changing. The person who changes the code is rarely the person who owns the page it affects, so the two almost never move together. A renamed environment variable, a new setup step, or a removed endpoint is a small code change and a docs change that quietly never gets made. The usual fixes each have a limit. "Docs are part of the PR" is the first thing cut under deadline. A scheduled audit finds drift late and in bulk, exactly when reconstructing what changed is hardest. And a generic writer pointed at the docs produces prose that doesn't match the code, because it never reads the code. What you want is for the docs you already have to stay accurate to the code, updated close to each merge rather than in periodic cleanups.
The docs AI Employee runs once a day, picking up from a checkpoint it kept from its previous run. It pulls every commit that landed on the default branch since that checkpoint, reads the surrounding code to understand intent (not just the diff), finds every page, README, and reference section that mentions the changed behavior, rewrites them to match, and opens a single documentation pull request with its reasoning attached. The code itself is read only to it, and nothing publishes without a named human's merge.
See exactly how the work gets done.
Runs on a daily schedule
A scheduled run fires once a day and resumes the work from where it left off. It reads a checkpoint from the previous run, then pulls every commit that landed on the default branch since, however many merges that turns out to be. Each affected page is handled as its own unit of work, so a problem with one never blocks the rest of the sweep, and the checkpoint advances at the end whether or not anything changed.
Writes to your standard, not its own
Your writing conventions (how the docs are structured, the terminology you use, which page covers which subject, and fixes that worked before) travel with the AI Employee as skills and memory that load into every run. It writes to that standard rather than inventing one, and the standard updates as docs pull requests get merged.
Connects to your systems, with permissions you set
It reads the diff and the surrounding codebase to understand what changed and why, searches the docs for every page that mentions the changed behavior, and opens a pull request on GitHub with the rewritten pages linked to the change that prompted them. Credentials stay in memory, never written to disk, never exposed to the model.
Never touches a branch anyone reads
Every change lands as a pull request gated on a human merge. It edits only files under the docs and README paths; the code itself is read only to it. There is no path by which it pushes directly to a branch a reader depends on.
Each merge updates the affected pages
The daily sweep triages what changed since the last checkpoint, finds the pages that drifted, rewrites them to match, and opens a docs PR with its reasoning attached. A renamed environment variable becomes an update to the setup guide; a new endpoint becomes a reference entry drafted from the actual handler; a removed feature becomes a PR that strips the stale section.
Runs in your environment
Each daily sweep runs inside your own infrastructure, resuming from a durable checkpoint rather than persisting raw repo state. It can read the whole repo to understand a change; only the docs pull request it opens leaves it.
Read only on the code
It edits only files under the docs and README paths. The code itself is read only to it. It can read anything to understand intent, but it cannot change a line of it.
PR gated
No change reaches a branch anyone reads without a person reviewing the diff and merging it. Publishing is always a human decision.
Credentials stay protected
The GitHub credential connects with the permissions you set; it stays in memory, never written to disk, and is never exposed to the model or the logs.
You own the rules
The AI Employee's persona, its skills, and its per system permissions are yours, versioned and changed on your terms, not in a vendor dashboard.
The backlog of "someone should update the README" now arrives as small, reviewable pull requests within a day of the code landing, with the reasoning written down. Your team reviews a diff instead of reconstructing months of drift, and readers stop hitting instructions that are no longer accurate.
Daily
Docs rechecked against the code that landed since the last run
Same day
Drift caught before it reaches a reader
3 systems
The diff, the codebase, and the docs, in one AI Employee
Our free AI audit shows you where AI fits, what your security risks are, and gets your first AI employee working.
How do AI Employees test every pull request before a human reviews it?
Checks out every pull request, runs the suite, exercises the change through the edge on a test deploy, and posts the result. It stays off production, leaving the merge to a person.
×How do you get a postmortem drafted the moment an incident resolves?
Reconstructs the incident timeline, correlates it against deploys and log spikes, and opens a structured postmortem as a doc PR. It drafts, and the team finalizes.
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How do you ship release notes without writing them from scratch?
On each release, reads the merged PRs since the last one, groups them by area, writes plain language notes, and opens a changelog PR. It never publishes, tags, or announces; a named human reviews and merges.
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