Your AI Writes Code Faster Than You Can Read. Here's What You Should Review Instead.

We are entering a phase of software engineering where AI can generate hundreds, sometimes thousands, of lines of code in minutes.

With tools like Cursor, Claude Code, GitHub Copilot, and other AI coding agents, implementation is becoming increasingly faster.

The speed at which AI produces code is quickly exceeding our ability to review it properly.

And I think this requires us to rethink what code review looks like.

I am not suggesting that developers should stop reading code. Understanding the implementation is still essential, especially when dealing with security-critical systems, financial transactions, authentication, and sensitive data.

However, when working with autonomous coding agents, reviewing every generated line is not necessarily the most effective way to establish confidence.

I would rather spend more time understanding the decisions behind the implementation.

Here are five things I think every engineer working with AI coding agents should start reviewing.

When an AI agent completes a task, I want to understand more than the files it created or modified.

A working implementation does not automatically mean a sound implementation.

For example, an agent might build an API integration that works perfectly during development but introduces unnecessary dependencies, tightly couples services, or creates a maintenance problem.

The important question is not simply whether the code works.

Can the agent explain the reasoning behind the system it built?

If it cannot explain its decisions, I have very little reason to trust the implementation.

This is one of the most important things I have learned to pay attention to when working with AI coding agents.

AI agents make decisions even when you haven't explicitly asked them to.

You ask for a feature, and the agent might independently decide to:

Some of these decisions might be reasonable. Others could introduce serious problems.

The challenge is that these decisions can be buried inside an otherwise successful implementation.

One question I would always ask an agent after implementation is:

"What decisions did you make that I did not explicitly instruct you to make? Explain why you made each decision and the alternatives you considered."

This forces the agent to expose assumptions and implementation choices that might otherwise go unnoticed.

Never assume that because you didn't specify something, the agent didn't make a decision about it.

I don't think the same agent that writes the code should be the only one responsible for validating it.

There is value in introducing an independent reviewer.

Have one agent implement the feature and another review the implementation.

Give the reviewing agent a different responsibility:

Most importantly, don't simply ask the second agent whether the code looks good.

"Review this implementation as a senior security and software architect. Do not assume the implementation is correct. Identify vulnerabilities, broken assumptions, missing edge cases, and architectural weaknesses. Prioritize findings by severity and recommend specific fixes."

A second agent is not a guarantee of correctness. Different agents can share the same blind spots, and AI-generated reviews can also produce false positives.

That is why critical findings still need human verification.

Sometimes, the easiest way to understand a system is to stop reading individual files and look at how everything connects.

Ask your AI agent to generate architecture diagrams based on the actual implementation.

A diagram might reveal that a callback endpoint can trigger a payment update without proper transaction verification.

That is much easier to spot when you can see the entire flow than when you are inspecting individual files.

A diagram also helps identify unnecessary dependencies, circular interactions, and components that have become too tightly coupled.

One important distinction: diagrams should be generated from the actual code and configuration, then verified. A beautiful diagram of what the agent intended to build is not evidence of what it actually implemented.

An application can be fast and still be fundamentally broken.

This is why I don't believe in evaluating AI-generated systems using a single performance metric.

For AI-powered systems, I would also monitor token consumption, model latency, tool-call success rates, hallucination rates, and task completion rates.

Consider an AI agent that processes 1,000 customer requests.

It might complete them quickly, but what if 15% of the requests contain incorrect information?

A system that is fast but unreliable is not necessarily a good system.

Performance without correctness is just a faster way to produce bad outcomes.

I believe AI is changing the responsibilities of software engineers.

We are gradually moving from spending most of our time writing implementation code to spending more time defining requirements, designing systems, evaluating decisions, validating outcomes, and managing risk.

This is not an argument for blindly trusting AI-generated code or abandoning code reviews.

You still need to understand the implementation. You still need to inspect critical code paths, run tests, validate security controls, and independently verify important behaviours.

The difference is where you invest your attention.

An AI agent can generate an implementation in minutes. But it cannot take responsibility for the consequences of deploying that implementation.

I would rather have an engineer who understands the system, challenges the agent's assumptions, and knows where the risks are than one who can generate thousands of lines of code but cannot explain what those lines do.

The future of software engineering isn't about who can generate code the fastest. It's about who can consistently turn generated code into reliable, secure, maintainable systems.

Before accepting an implementation from Cursor, Claude Code, or another coding agent, try asking:

Act as a senior software architect conducting a post-implementation review.

Do not just summarize the files you modified.

Be critical. Distinguish verified findings from assumptions. Never claim that something has been tested or validated unless you actually performed the test.

Rank all findings by severity: Critical, High, Medium, and Low.

Do not modify any code during this review. Report your findings and recommend specific fixes first.

Don't let AI make you a faster code producer but a weaker engineer.

Use it to increase your leverage, not to outsource your engineering judgment.

What are you reviewing when your AI coding agent tells you it has finished?