I started coding shortly before GPT-3.5 was released, so I learned the fundamentals before AI-assisted development became common. These days I use AI heavily: I describe what I want, let it generate code, and then inspect the changes carefully. My current setup is VS Code with a Claude Code extension, reviewing the generated diffs through Git tools.
A newer coworker follows a very different process. They no longer read the generated code and instead have another model review it. I'm also seeing more developers and technology leaders describe similar workflows, where people focus on guiding agents and making architectural decisions rather than inspecting every implementation detail. Some prefer agent-focused tools such as Claude Code, Cursor, or desktop applications instead of a traditional editor.
Is my workflow already outdated, or is reviewing the actual code still an important part of responsible development? I'm interested in how others balance AI assistance, automated review, testing, and human understanding of the code they ship.
4 Answers
There’s no universal race where the newest workflow automatically wins. If your process produces reliable code, meets your team’s requirements, and keeps you productive, it’s valid. Do experiment with other tools occasionally, because the technology changes quickly, but optimize for outcomes rather than copying whatever workflow is currently fashionable.
AI review can be a strong second layer, but it shouldn’t replace your own review. A practical workflow is to have an agent plan the change, inspect the plan yourself, let AI implement it, run tests and static checks, and then review the diff personally. You don’t need to reread every familiar line forever, but you should understand the important changes and remain capable of debugging them later.
The more you automate, the more important your safeguards become. Strong tests, type checking, linting, dependency and security scans, architecture rules, and complexity limits can reduce the damage from bad generated code. Those checks let agents work faster, but they are a safety net rather than proof that the code is correct. Someone still needs to own the result and understand enough of it to maintain it.
Your workflow isn’t outdated. The editor or AI tool is mostly a matter of preference; the important question is whether someone responsible understands and validates what reaches production. Having one model generate code and another model review it can catch useful issues, but both can share similar blind spots. Human review is still important for architecture, security, assumptions about the environment, and behavior that automated tools cannot observe.
Automated review is especially limited when the reviewer cannot access private configuration, production behavior, or the business context behind a decision. It may accept a confident but incorrect explanation without being able to verify it.
Agents often find bugs I miss, so I use them as additional reviewers. I still check for questionable shortcuts, unwanted complexity, security problems, and whether the resulting code fits the existing system.