AI writes the code, humans decide: where expert reviews fit in an agentic workflow

The most common fear about agentic development is not "AI will write bad code". It is "AI will write code behind my back, and I will no longer know why". A black box that produces code you cannot discuss, and cannot have reviewed by someone you trust. That fear is legitimate - and it is precisely what Survol is built to avoid.

Our conviction: humans keep control. Not as a reassuring slogan, but as a concrete mechanism, at two points in the workflow: before the agent writes code, and after.

Delegating execution is not delegating the decision.

Before: the product decision is discussed between humans

At Survol we never start an agent session on an unsettled question. Product decisions are discussed - as a team, and if useful with the agent as a contributor, never as a referee. A decision has a clear lifecycle: open → accepted / rejected → implemented, with its dates, a thumbs up/down vote, the list of voters, and an optional link to a feature. Once it is settled, the decision feeds the agent's context. Not before.

After: the expert review becomes tasks

Code is not sacred just because an agent wrote it. Survol includes human expert reviews: you order a report from a domain expert, who returns findings graded by severity - critical, important, minor - with a proposed fix for each one.

And that is where the loop closes cleanly: every finding is actionable in one click. From a finding you create either a task for the agent, or a product decision if something has to be settled first. Comments stay shared with the expert. The report is not a PDF sleeping in a drawer: it is an entry point into the work.

# An expert report, turned into actions
finding [critical]  "Possible SQL injection in search"      → agent task
finding [important] "No pagination on the history view"     → product decision
finding [minor]     "Inconsistent labels at checkout"       → agent task

Verification, not just code

Trust is not declared, it is traced. Every version carries its verification state: who reviewed the code (a human or an agent - useful on manual versions), which test books were run, by whom and on which environment, who checked in production. The direction we are heading: turning those verifications into configurable gates before promotion - for example, forbidding a production release until the preview test book has passed.

The takeaway

1. Product decisions are made between humans, before the agent is launched.
2. An expert review converts, finding by finding, into tasks or decisions.
3. Every version keeps a record of who verified what - trust is documented, not assumed.

A good agentic tool does not try to replace human judgement; it puts it in the right place and makes it effective. AI writes code fast - all the better. But it is still a human who decides what deserves to be written, and a human who says "yes, this can go to production". Survol simply equips those two moments. That is all, and that is the essential part.