AI Agent Review Screens Should Highlight What Was Not Inspected, Not Just What Was

A software design perspective published on DEV Community argues that AI agent review interfaces over-emphasize completed actions while obscuring gaps in inspection. The author contends that hidden absence—such as files outside an agent's granted workspace or unavailable integration tests—poses a greater risk than acknowledged gaps. The piece proposes grouping review evidence by decision consequence rather than chronological activity logs, and replacing vague percentage-based coverage metrics with named risk-area breakdowns. It also recommends that approval actions clearly define their scope, so reviewers cannot inadvertently approve changes that implicitly cover unverified areas. The author concludes that reviewer trust is built by transparently presenting the boundaries of an agent's inspection, enabling narrower and more accountable decisions.