AI Coding Agents Are Fast. Your Review Process Needs to Be Faster.

AI coding agents are getting good enough that “write the code” is no longer the interesting part.

The harder problem is knowing whether the code is correct before you trust it.

That matters because AI-assisted development is no longer a niche workflow. Stack Overflow’s 2026 Developer Survey reports that 66% of respondents use AI coding assistants or agents, and 73% of those users use them daily. At the same time, developers still tend to trust AI more for technical tasks with checkable answers than for ambiguous work.

So I’ve been thinking about a simple rule for working with coding agents:

Don’t ask an agent only for code. Ask it for evidence.

What “evidence” means

Suppose you ask an agent:

Add input validation to this form.

A weak workflow ends when the agent says it is done.

A stronger workflow asks for:

1. The implementation.
2. The tests it added or changed.
3. The edge cases it considered.
4. The commands it ran.
5. The actual test output.
6. Any assumptions it made.

That small change turns the agent from a code generator into something closer to a junior engineer who must show their work.

A practical prompt pattern

For small features, I like a structure like this:

Implement this feature.

Before changing code:
- inspect the relevant files
- explain the current behavior briefly
- identify likely edge cases

After changing code:
- add or update tests
- run the relevant test suite
- report the exact commands you ran
- report failures honestly
- summarize assumptions and remaining risks

Do not claim a test passed unless you actually ran it.

The last line is particularly important. An agent can produce a convincing explanation without having executed anything.

Why this works

AI-generated code is easiest to trust when the result is independently checkable.

For example, these tasks have relatively clear feedback loops:

• unit tests
• type checking
• linting
• build commands
• formatting checks
• browser tests
• API contract tests

The agent can make a change, run the check, see the result, and iterate.

The situation is very different when the question is something like:

“Is this architecture going to scale for the next three years?”

There may be no single command that proves the answer.

That is where human judgment becomes much more important.

The review loop I would use

For an AI-assisted feature, a useful loop is:

Plan → Implement → Test → Inspect → Review → Merge

The key is not to remove the human from the loop. It is to move the human toward the decisions where human judgment has the most value.

Instead of spending 20 minutes checking whether a missing semicolon broke the build, let automated checks handle that.

Spend those 20 minutes asking:

• Did we solve the right problem?
• Is this change simpler than the alternatives?
• Does the code fit the existing architecture?
• What happens with unexpected input?
• What happens when an external service fails?
• Did the agent introduce a security or privacy problem?

One more useful habit: ask for a diff summary

Before reviewing a large AI-generated change, ask the agent to summarize the diff in plain language:

Review your own changes.

Give me:
- files changed
- behavior changed
- tests added/updated
- potential regressions
- security-sensitive changes
- anything you are uncertain about

This is not a replacement for reading the diff. It is a way to decide where to read the diff first.

The bigger shift

The interesting change in agentic coding is not simply that machines can write more code.

It is that the developer's job increasingly becomes orchestration and verification.

Current research reflects that shift. Anthropic's 2026 agentic coding report describes software development moving toward orchestrating agents while keeping human judgment and oversight in the loop. JetBrains' 2026 developer survey also reports widespread weekly and daily use of AI coding agents among professional developers.

That suggests a useful skill for developers to build now:

Learn how to design good verification loops, not just good prompts.

A clever prompt can get you a better first draft. A good verification loop tells you whether that draft deserves to ship.

Final thought

AI coding agents can make implementation dramatically cheaper. That makes verification more valuable, not less.

If an agent can write 500 lines in a few minutes, the scarce resource is no longer typing those 500 lines.

It is your confidence that they should exist.

What is your best technique for verifying code written by AI agents?