How do AI Employees test every pull request before a human reviews it?
A QA AI Employee that checks out each pull request, runs the full suite, exercises the change on a live test deploy, and posts a pass/fail result. Production is out of scope, and a named human owns the merge.
×
Code review catches the problems a person can find by reading a diff. It misses the ones you only find by running the change: a test that passes locally but flakes in CI, a path that works in the happy case and 500s on an edge case, a migration that reads fine but locks a table under load. A reviewer reads the code and approves, but no one has checked out the branch, run the suite, hit the new endpoint, and watched what happens. Those failures surface in staging or production, after the PR is approved. The common fixes are incomplete. Green CI shows the existing tests pass, not that the change is correct. It runs the tests the author wrote, not the paths they missed. A manual QA pass is thorough but slow and lands after the review. And a generic reviewer only sees the diff; it can't run the branch, so it never catches a failure that only appears at runtime.
The QA AI Employee runs on every pull request, on open and on every push. Each run works one branch in its own isolated run: it checks out the change and installs it clean, runs the full suite (unit, integration, and end to end), stands the change up on an ephemeral test deploy, exercises the new behavior through the edge (routing, headers, caching, redirects), and posts a result on the PR. Green when it passes; a red check with the failing command, the logs, and steps to reproduce when it doesn't. It stays on the test environment, never deploys to production, and never merges.
See exactly how the work gets done.
Runs on every pull request
A signed webhook from GitHub triggers a run on every pull request opened or updated, seeded with the branch under test. One PR maps to one isolated run, so nothing carries over between runs and concurrent PRs run in parallel.
Works to your QA playbook
Your QA conventions travel with the AI Employee as skills and memory: how to run the suite, which flows are critical, edge cases that have caused problems before, and what a result should contain. When a bug slips through, you write it down and the AI Employee picks it up on the next run.
Connects to your systems, with permissions you set
It runs the full suite in its own environment with the failure output captured in full, deploys the change to an ephemeral test environment and exercises it end to end, checks behavior through the edge rather than just localhost, and posts the pass/fail result and any reproduction as a check and a comment on GitHub. Credentials stay in memory, never written to disk, never exposed to the model.
Stops at the test plane
Its access ends at the test environment: no production access, no production deploy, no merge. Its output is a result; a named human owns the decision to merge.
Each PR arrives already QA'd
A new PR checks itself out, runs the suite, deploys to the test environment, exercises the change through the edge, and posts a result. A flaky test is flagged with the evidence; a broken edge route is caught before staging.
Runs in your environment
Every pull request runs in its own isolated run on its own branch, inside your own infrastructure. It can install, deploy, and exercise the change to reproduce a failure; only the reported result leaves it.
Credentials stay protected
The GitHub, test environment, and edge credentials connect with the permissions you set; they stay in memory, never written to disk, and are never exposed to the model or the logs.
Test environment only
Its access stops at the test plane: no production access, no production deploy, no merge. It reports; your team decides.
The merge waits for a signature
The AI Employee's output is a result, never a merge. The decision to ship a change is always a named human's.
You own the rules
The AI Employee's configuration, its skills, and its permissions are yours, versioned and changed on your terms, not in a vendor dashboard.
Runtime failures that a diff can't show now surface on the pull request when it opens, with the failing command, the logs, and the repro attached. Reviewers spend their time on design rather than checking out branches by hand, and the changes that reach staging have already been run against the test environment.
Every PR
Run, deployed, and exercised before human review
Before merge
Runtime failures caught before they reach staging
4 systems
Branch, suite, test environment, and edge 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 keep your docs in sync with the code?
Sweeps the code merged each day, rewrites the docs those changes touched, and opens one reviewable pull request. Publishing waits for a human merge.
×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.
×
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.
×