Deploys that watch themselves
and undo the ones that go wrong
A bad deploy usually announces itself within minutes, a spike in errors, latency climbing, a health check failing, but by the time a person notices and decides to roll back, real users have already hit it. We build an agent that watches the minutes right after every release and rolls back automatically the moment your own thresholds are crossed.
The process today
A release goes out, and the team that shipped it moves on to the next task, trusting that someone will notice if something breaks. Sometimes that someone is a customer filing a support ticket twenty minutes later, sometimes it is a monitoring alert that fires but gets missed in a busy channel, and sometimes it is nobody until the next morning’s metrics review. Either way, the gap between a bad deploy going out and a person deciding to roll it back is almost always longer than it needs to be.
The second cost is the decision itself. Rolling back is a judgment call under pressure: is this error rate a real problem or normal noise, is it worth reverting a release that also fixed something important, who has the authority to make that call at 11pm. Teams without a clear automatic rule end up debating this in the moment, which costs more time than the rollback itself.
The third is that manual rollback tends to be an afterthought in the deploy process, a runbook step that works but is rarely rehearsed, so the one time it is actually needed under pressure is also the first time in months anyone has run it.
What the agent does
The agent watches a defined window right after every deploy, typically five to fifteen minutes, against your error rate, latency and health-endpoint baselines. If your platform supports canary or percentage-based rollout, it watches the canary slice first and only promotes the release to full traffic once it holds steady. The moment a threshold you have set is crossed, error rate above baseline, latency climbing past an agreed limit, a health check failing repeatedly, it triggers an automatic rollback to the last known-good version and posts a plain-language note explaining what tripped it and what the numbers looked like.
Deploys that pass the health check window are logged as clean, with the same metrics kept for trend comparison on future releases, so your baselines improve over time instead of staying fixed at whatever they were set to on day one. Typical integrations: your existing deploy pipeline (GitHub Actions, GitLab CI, a deploy script), your monitoring stack for the metrics, and Slack or Telegram for the rollback notice.
What stays with humans
The thresholds that define a bad deploy are set and owned by your team, not guessed by the agent; a false positive it was set up to avoid is a tuning conversation, not a surprise. A rollback that happens mid-incident still needs a person to decide the next step, whether to fix forward, re-deploy a patched version, or investigate further; the agent buys time by stopping the bleeding, it does not diagnose the root cause. Any change to the rollback thresholds themselves is a deliberate decision your team makes, logged like any other config change.
Guards
Every deploy’s health check window and outcome is logged, clean or rolled back, with the metrics that drove the decision, so the history is auditable. The agent runs in observe-only mode against several real deploys before it is given the ability to trigger a rollback automatically, so you see how it would have called recent history first. A manual override and a forced rollback command are always available regardless of what the automatic system is doing, and a kill switch disables automatic rollback in one message if you need to deploy something you know will trip a threshold on purpose.
Price and timeline
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| Single automation | from $1,000 | One service, health check window, automatic rollback, deploy history | 1 to 2 weeks |
| Department package | from $2,800 | Rollback automation plus CI/CD checks and post-release smoke tests | 2 to 4 weeks |
Running cost is usually $15 to $40 a month in model and monitoring usage depending on deploy frequency.
Related
Pair this with CI/CD pipelines with AI code checks so a risky change is flagged before it ever reaches a deploy, and with post-release smoke tests to confirm the basics work before the health check window even starts. For the infrastructure side of the same releases, see server and infrastructure health monitoring. Full package details are on the AI agents service page and the automation-everything overview; for how this looks on infrastructure we run ourselves, see the secure infrastructure case study and the factory ERP recovery case study.
Tired of finding out about bad deploys from a customer? Get in touch and we will map your current deploy and rollback process.
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FAQ
How much does automated rollback cost to set up?
From $1,000 for one service and deployment pipeline, live in 1 to 2 weeks. Multiple services sharing the same rollback logic usually run $1,800 to $2,800.
What counts as a bad deploy that triggers a rollback?
Whatever you define: error rate above a threshold, latency above a baseline, a failing health check, a spike in a specific log pattern. We set these with you before launch and tune them against your real traffic.
Which deployment platforms does this work with?
Kubernetes, Docker Compose on a VPS, Vercel, Render, or a custom deploy script, as long as there is a way to redeploy the previous version programmatically.
Can a rollback happen by mistake on a slow but healthy deploy?
Thresholds are set conservatively and tuned against your actual traffic patterns before going live, and a short grace window absorbs normal post-deploy noise like cache warming, so a healthy deploy that is just slow to warm up is not mistaken for a broken one.
Can we still roll back manually?
Yes, a manual forced rollback is always available in one command or one message, independent of whatever the automatic thresholds are doing.