Reviewing AI-Assisted Code Before It Ships

After months of working with agentic coding tools every day, I’ve changed how I think about their value.

The biggest benefit is the amount of a system I can understand, review, and improve. I can follow a feature across backend, web, iOS, and Android, then use the agent to help bring those implementations into alignment.

That wider reach also makes the review decision important: what evidence tells me a change is ready to ship?

This guide sets out the questions I use to think about that decision. It is a review framework, not a guarantee that every defect will be caught or a report of measured productivity gains.

Establish the Intended Behavior

Before judging an implementation, I need to know what it is supposed to do. A clear task should identify the business rule, the affected users, the constraints, and what is outside the change.

Otherwise, an agent can implement a plausible interpretation that does not match the product. The resulting code may be tidy and internally consistent while still answering the wrong question.

For an inherited system, an existing behavior is evidence of what the software does today. It is not, by itself, proof of what the business wants it to do.

Review the Change in Context

I want to inspect the actual diff and the behavior around it, not just a summary of completed work.

• Does the change fit the API contract and data model?
• Is an existing rule being reused, or has a second version appeared?
• Do permission and validation checks still happen in the right place?
• Are all affected clients accounted for?
• Has unrelated cleanup made a focused change harder to review?

When the affected consumers are unclear, I start with a bounded codebase investigation. That article explains how I use an agent to trace dependencies before deciding which files need changes.

Finding more code does not mean editing more code. The scope should follow the product problem and the evidence.

Read What the Tests Actually Prove

A passing test is useful only in relation to its assertions and setup. A test that repeats the implementation’s assumptions can preserve the same mistake.

The questions I would ask during review are:

• Does the test assert the intended business behavior?
• Does it cover the failure or edge case this change addresses?
• Are mocks hiding the integration that needs verification?
• Was the relevant test actually run against this change?

Not every edit needs the same test strategy. A narrow logic change and an authentication flow across clients have different risks. The verification should be chosen for the behavior that could break, rather than for the number of tests produced.

Check the Running System When Source Is Not Enough

A build confirms something different from a working user flow. Configuration, credentials, sessions, and deployment conditions can determine whether apparently correct code runs as intended.

In two mobile debugging examples, I describe a notification path whose Firebase Admin client was uninitialized and an Android image request that lacked its session cookie. The useful clues were the runtime state and the HTTP response.

Those examples are not evidence that every problem requires the same fix. They show why a review sometimes has to leave the diff and follow the failing request.

Make the Remaining Uncertainty Explicit

A useful handoff should let another person understand both the change and the limits of its verification. I would want it to answer:

• What changed, and why?
• What tests and runtime checks were completed?
• What expected behavior did those checks demonstrate?
• What could not be checked in the available environment?
• What remains a separate issue rather than part of this patch?

That makes the release decision concrete. An untested environment or unresolved product rule should remain visible, even when the implementation looks finished.

Where Engineering Judgment Fits

Early on, I looked at inconsistent AI-generated projects and assumed agentic coding was the problem. I now think that explanation was too simple. Tools can accelerate useful work, but someone still has to own product intent, architecture, and the decision to release.

The advantage I value is being able to apply that judgment across more of the system. AI helps with investigation, implementation, and verification; review connects that work to the product it needs to serve.

If your existing application has reached the point where nobody is confident what can safely ship, the App Rescue Assessment offers a bounded technical diagnosis with prioritized findings and next steps. It is an assessment, not an implementation package or a promise that every issue can be resolved within it.