Verification Loops for AI-Written Web Code

AI coding agents produce code faster than any team can read it. Diffs pile up, the QA list grows, and senior engineers drift into full-time reviewers. The instinct is to slow down and read every line. That feels responsible, but it caps the whole project at the speed of human reading. The deeper issue is not that the code is bad. It is that nothing besides a person's eyes is establishing trust.

At Curiosive we treat that as a process problem. Rigour does not disappear when we stop line-by-line reading. It moves to two places where it compounds: before the build and after it.

Put the rigour into the spec before the build

Trust starts with a plan the agent can follow. For a web product, that means a written requirements document covering the features, the data model and the relationships between entities. It also means a short list of milestones, each with an explicit statement of what is in scope and what is not.

The agent then adds a second layer: an implementation plan for each milestone, produced in a planning mode before any code is written. When the spec is detailed, that plan is easy to check against it. A quick read for odd naming or misplaced responsibilities is usually enough. Prescriptive instructions about how every function should be written tend to get in the way of capable models, while a clear definition of the outcome helps them.

One habit that pays off is a milestone log. As the agent finishes each milestone, it writes brief notes on what was built, where it stopped and which small technical decisions it made. The log serves two readers. A human gets a fast summary to test against. The next agent session gets the context it would otherwise lack.

Build verification loops the agent runs itself

The second half is evidence. Standing instructions in the project's agent configuration file can require that a feature is not reported as done until the full test suite passes. Tests alone miss the interface, though, so the same instructions can require browser-driven checks for any UI change.

An agent equipped with a browser automation tool can open the app, click through user flows, fill in forms and check accessibility. It can also capture screenshots of new pages and layouts, then compare them with the spec. Spacing, button placement and layout problems that a reviewer would normally spot by eye get caught before a human sees the build.

This does not remove people from the loop. Someone still clicks through the critical paths before a milestone is accepted. The refinements that remain tend to be things nobody could have foreseen until the feature ran in a real browser, rather than bugs the agent overlooked.

What this means for web development practice

For founders and product teams, the practical consequence is scope. When trust comes from planning and verification, the size of what a team can attempt is no longer limited by review hours. Work that once felt reckless becomes manageable.

This is not vibe coding. Humans own the what (the product, the spec, the architecture) and the proof (the checks that demonstrate it works). The how, meaning the code itself, can be delegated with confidence once those two are solid. A sensible starting point is small: write a scoped spec for the next feature, require tests to pass, add one automated browser check for the main user flow, and keep a log. Then see how much review time disappears.