The basics checked live,
seconds after every release ships
A test suite can pass completely and a release can still break checkout in production, because a config difference, a missing environment variable, or a third-party integration behaving differently in the live environment is exactly what a test suite run before deploy cannot see. We build an agent that walks through your critical user flows against the live environment right after every release and alerts immediately if any of them fail.
The process today
A release passes every test in the pipeline and ships, and the first real confirmation that it actually works comes from either a person manually clicking through the app after deploy, which depends on someone remembering to do it and having the patience to do it thoroughly every single time, or from customers, who notice when checkout is broken far faster than any internal process does.
The second cost is that the gap between a test suite passing and a production environment actually working is specifically where configuration lives: an environment variable that exists in staging but was never set in production, a third-party API key that is valid in one environment and expired in another, a feature flag left in the wrong state. None of these fail a build or a pre-deploy test, because the test environment does not have the same configuration gap.
The third is that even when manual post-deploy checking does happen, it tends to shrink under time pressure to “did the homepage load” rather than actually walking through checkout, login, or whatever flow is genuinely critical to the business, which means the checks that do happen are not checking the things that would actually hurt if broken.
What the agent does
Right after every deploy, the agent walks through your defined critical user flows against the live production environment using dedicated test accounts and synthetic data, never real customer records. Checkout, login, signup, or whatever your team defines as genuinely business-critical, each gets exercised the way a real user would, including any third-party integration involved, a payment provider, an email delivery service, anything a silent configuration mismatch could break without triggering any other alert.
If a flow fails, the alert names the exact step, not just that something is wrong: which page, which action, which error. Where automatic rollback is already set up, a failed smoke test can trigger it directly; otherwise it pages whoever is on call immediately with enough specificity to start investigating right away instead of starting from scratch. The same checks also run on a schedule between deploys, catching drift that is not tied to a release at all, a third-party API that changed behavior on its own schedule, a certificate that silently broke an integration. A pass and fail history per flow builds a reliability record over time. Typical integrations: your deploy pipeline for the trigger, a browser automation tool for the actual flow walkthrough, and Slack, Telegram or PagerDuty for alerts.
What stays with humans
Defining which flows are actually critical enough to warrant a smoke test, and keeping that list current as the product changes, is a decision your team makes and periodically revisits; the agent runs whatever is defined, it does not decide what matters to your business. Diagnosing and fixing the root cause of a failed flow is engineering work; the smoke test narrows down exactly where the break is, which is most of the value, but a person still does the fix.
Guards
Every smoke test run, pass or fail, is logged with the specific step and timing, building a reliability history per flow that is useful well beyond any single incident. Test accounts and synthetic data are kept strictly separate from real customer data, with no path for a smoke test to touch a live customer record. A kill switch pauses smoke tests during a planned maintenance window where a flow is expected to be temporarily broken, without losing the history already recorded.
Price and timeline
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| Single automation | from $600 | Your core critical flows, post-deploy and scheduled runs, immediate alerts | 4 to 8 days |
| Department package | from $1,800 | Smoke tests plus automated deployments with rollback and performance regression alerts | 2 to 3 weeks |
Running cost is usually $10 to $30 a month in browser-automation and model usage depending on deploy frequency.
Related
This pairs well with automated deployments with rollback so a failed smoke test can trigger an immediate revert, and with schema and contract tests for the integration layer underneath these flows. For the pre-deploy side of the same safety net, see CI/CD pipelines with AI code checks. Full package details are on the AI agents service page and the automation-everything overview; for products where this kind of live verification matters directly to revenue, see the ProBay AI agent team case study and the secure infrastructure case study.
Ever had a release pass every test and still break checkout? Get in touch and we will set up a smoke test for your critical flows.
Tired of doing this by hand? We can take the whole routine off your team, not just this step: Routine takeover, from $400 →
FAQ
How is this different from our existing test suite?
Your test suite runs before deploy, usually against a test or staging environment. This runs after deploy, against the real live environment with real configuration and real third-party connections, which is where config and environment differences actually show up.
How much does post-release smoke testing cost?
From $600 for your core critical flows, live in 4 to 8 days. A more complex application with many critical paths usually runs $1,000 to $1,800.
Does it use real customer accounts or data to test?
No, dedicated test accounts and synthetic data are used specifically so the smoke test never touches real customer records, while still exercising the actual production systems and integrations.
What happens when a smoke test fails?
An immediate alert fires naming the exact step that failed, and where automated rollback is already in place, that can trigger directly; otherwise it pages whoever is on call with enough detail to start investigating right away.
Can this run on a schedule too, not just after a deploy?
Yes, scheduled runs between deploys catch environment drift unrelated to a release, a third-party API that changed behavior, a certificate that silently broke a payment integration, issues a deploy-triggered check alone would miss.