AI coding agents are changing the unit of work developers delegate. Instead of asking for one function, teams can ask an agent to investigate a bug, edit several files, and run checks. The difficult question comes afterward: what evidence is enough to accept the result?

GitHub's research on validating agentic behavior describes why checking a single expected sequence of actions is brittle: agents can take different paths to the same result. Its proposed validation work separates successful outcomes from particular execution traces. That is a useful starting point for reviewing agent-written software. Read GitHub's research.

The workflow below is a practical recommendation for application teams, rather than a claim that any agent can work without supervision.

Define the result before delegating

“Fix the search screen” leaves too much room for interpretation. A stronger task describes observable behavior: submitting a blank query should show guidance, a failed request should show a retry action, and a slower old response must not replace a newer result.

Also name constraints. A bug fix should not silently change the API contract or introduce a new state management library. These boundaries make the eventual diff easier to judge.

For Android applications, the separation described in Clean Architecture for Android provides useful places to define those boundaries: screen behavior, application logic, and data access.

Review the evidence independently

An agent's final message is an index of claims to verify. Start with the changed files and ask whether each change contributes to the requested behavior.

Then inspect the tests. A passing test that merely repeats the implementation can preserve the same mistake. Prefer tests derived from the requirement, including a failing or unusual case.

For the search example, deliberately delay the first response and complete the second one first. This checks a user-visible failure that a normal happy-path test misses. The result matters more than the agent's confidence.

Make the next attempt smaller

If the patch mixes a dependency upgrade, a UI redesign, and the original bug fix, reduce the scope before spending an afternoon reviewing it. Request one coherent change with a clear before-and-after example.

Keep a checkpoint of the original code, record the commands used for verification, and preserve relevant failure output. These habits make another iteration easier for both a person and an agent.

Measure accepted work

Generated lines of code are a poor success measure. Consider tracking review time, regressions discovered after merge, and the proportion of proposed changes accepted without substantial rewriting.

A small experiment can be enough: use an agent for a few similar maintenance tasks, keep normal review standards, and compare the full time to completion. Include investigation and cleanup, not just the time until a patch appears.

The useful skill is learning to specify and evaluate delegated work. Start with a bounded task whose outcome you can verify, then expand responsibility as the evidence supports it.