Verification engineering

Writing code has become cheap. A coding agent can open a pull request in minutes. Knowing that the pull request works has not become cheap at all, and verification engineering is the work of closing that gap: deciding what “works” means for a product, checking it automatically on every change, watching it after it ships, and feeding what breaks back to whoever, or whatever, writes the fix.

It is broader than testing in the usual sense. A test suite is one artifact. Verification is the whole system that tells you, with evidence, whether a change is safe to ship and whether what is already live still works.

When engineers wrote every line, code review and a handful of tests could keep pace with the rate of change. As coding agents write a growing share of the code, the volume of change outgrows what people can review by reading it, and the bottleneck moves from writing software to verifying it.

Teams that can verify a change quickly can ship it quickly. Teams that can’t are left with two choices: slow the agents down to the speed of human review, or merge without knowing.

• A map of what users do. The journeys that matter, such as signing up, checking out or inviting a teammate, on the devices real users have.
• A check on every change. Each pull request run against those journeys and compared with production, so a regression is caught before merge rather than after.
• Verification in production. The same journeys on a schedule, so a bad deploy or a broken dependency is found by the team, not by its users.
• Evidence, not just a verdict. The recording, the steps and the device, so a failure can be understood without reproducing it by hand.
• A loop back to the fix. Findings precise enough to hand to an engineer, or to a coding agent as a prompt.

Hand-written end-to-end tests cover what someone thought to write down. They break when the interface changes, and they fall behind the product. Tools that let you write tests in plain English make each test easier to write, but someone still has to describe every one.

Verification engineering asks a different question. Not “did my tests pass?” but “does the product still work for the people who use it?” Answering it means discovering the journeys users actually take, not only running the ones someone wrote down.

Simulithic is AI-native verification infrastructure. AI agents audit your app and map every user journey, with no scripts or prompts to write. Every pull request is tested on its preview and on production, per device, and gets one comment that says what broke. Monitors re-run the journeys in production as often as every 30 minutes. Every finding comes with the recording and a fix prompt for your coding agent.

See how it works, read the getting started guide, or book a demo.

Is verification engineering the same as QA? QA is often a stage at the end, a person or a team checking a release before it goes out. Verification engineering builds the checks into every change, so most of that work is automated and happens before merge.

Does it replace unit tests? No. Unit tests check code in isolation. Verification engineering is about whether the product works end to end for its users. Most teams keep both.

Does it need a dedicated team? It can be owned by the engineers who ship the code. The point of automating it is that verification keeps pace with the change, whoever, or whatever, wrote it.