Every pull request reviewed
before it reaches your pipeline
Most CI/CD pipelines run tests but skip the judgment calls. Is this diff actually safe to merge? Did it touch a file nobody reviewed carefully? Does it change a config that has broken production before? We build an agent that sits in your pipeline, reads every diff, runs your existing checks, and adds a reasoned pass or block decision with the reasoning attached.
Why a green test suite isn’t the whole story
A pull request opens, the test suite runs, and if it’s green, most teams merge without anyone reading the actual diff line by line. Especially under deadline pressure. That works until the change that breaks production is exactly the kind tests don’t catch. A config value changed for one environment. A migration that’s safe in isolation but not alongside another pending one. A dependency bump that passes tests but changes behavior nobody noticed.
Who ends up reviewing is its own problem. On a small team, the same one or two senior people read every diff that matters. That makes them a bottleneck, and junior contributors wait hours for a merge a more experienced eye would clear in two minutes. On a larger team, review quality varies by who happens to be free, not by how risky the change actually is.
The checks a pipeline runs are static, too: the same lint and test suite no matter what changed. A one-line typo fix and a change to the payment webhook handler get the same automated nod. They clearly deserve different levels of scrutiny.
How the agent reads a diff before you do
The agent watches pull requests as they open. It reads the full diff alongside the files it touches and their recent history. It also runs your existing lint, type-check and test suite, same as today. On top of that, it produces a risk read. Does this change touch a migration, a payment path, an auth check, a production config, a dependency with a known history of breaking changes in your stack. Routine changes, formatting, copy, a contained bug fix with passing tests, get a clear pass with a short summary of what changed. Anything flagged as higher risk gets a plain-language note attached to the pull request. It explains exactly what concerned it and why, for a human to read before approving.
Say your team has a documented pattern of past incidents: a migration that went out without a rollback plan, a config change that took down staging. The agent checks new diffs against that pattern specifically. It blocks a merge that matches it until someone signs off. Typical connections: GitHub Actions or GitLab CI as the pipeline, Slack or Telegram for the summary and any block notification.
What stays with your senior reviewers
The agent adds a layer of judgment on top of your existing checks. It does not replace a senior reviewer’s sign-off on anything genuinely risky. It also does not decide your team’s standards for what counts as risky in the first place. Those thresholds are set by you and tuned over the first few weeks. Final approval on anything touching money, auth, or a production migration always needs a named human approver, not just an AI pass.
Guards
Every pass and block decision is logged with the reasoning behind it, so any call can be reviewed after the fact. The agent runs in shadow mode against your last month of merged pull requests before it’s given the ability to block anything live. You see how it would have called real history before it calls anything new. A kill switch turns the gate off in one message, reverting to your existing checks only, with nothing lost.
Price and timeline
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| Single automation | from $900 | One repository, risk scoring, block rules, Slack or Telegram summaries | 1 to 2 weeks |
| Department package | from $2,500 | CI/CD checks plus automated deployments with rollback and post-release smoke tests | 2 to 4 weeks |
Running cost is usually $20 to $60 a month in model usage depending on how many pull requests the agent reads.
Related
This pairs naturally with automated deployments with rollback, so a change that passes review also ships safely. Add post-release smoke tests for a check on the other side of the deploy. For the data layer specifically, see database migrations with safety checks.
Full package details are on the AI agents service page and the automation-everything overview. For a sense of how we run infrastructure for our own products, see the secure infrastructure case study and the ProBay AI agent team case study.
Want fewer production surprises from routine merges? Get in touch and we will look at your pipeline and your last few incidents.
Tired of doing this by hand? We can take the whole routine off your team, not only this step: Routine takeover, from $400 →
FAQ
How much does it cost to add AI checks to a CI/CD pipeline?
From $900 for one repository and pipeline, live in 1 to 2 weeks. Multiple repositories or a shared policy across a monorepo usually run $1,500 to $2,500.
Does this replace our existing tests and linters?
No. It runs alongside them and reads their output. Then it adds a layer of judgment on top: whether the overall change is risky given what it touches, not just whether individual checks passed.
Which CI systems does it work with?
GitHub Actions, GitLab CI, Bitbucket Pipelines, Jenkins, or CircleCI. It reads your pipeline's existing config and output rather than requiring a migration to a new runner.
Can it actually block a merge?
Yes, as a required status check, the same mechanism your tests already use. You set the risk threshold. Anything below it merges normally. Anything above it needs a human sign-off.
What happens when the agent gets a risk call wrong?
Every decision is logged with its reasoning, so a wrong call is easy to spot and correct. The pattern that caused it gets added to the rules, so the same mistake does not repeat.