--- title: "Why I Don't Review Every Line of AI-Generated Code" description: "I think understanding the system, testing it properly, managing agent context, and keeping strong computer science fundamentals matter more than manually inspecting every generated line." date: 2026-10-22 topic: agents tags: ["ai-coding-agents","code-review","software-architecture","automated-testing","context-management","computer-science"] production: human source: https://notes.suffro.com/posts/why-i-dont-review-every-line-of-ai-generated-code --- # Why I Don't Review Every Line of AI-Generated Code I don't think manually reviewing every piece of code generated by AI makes much sense anymore. In fact, I think doing it by default can be counterproductive. I'm not saying developers should stop reading code. If I have doubts about a specific implementation, if an architectural decision needs my attention, or if I don't understand something important, I absolutely want to inspect it. Being able to open the code and understand what is happening remains essential. What I question is the idea that you need to go through every generated line simply because an AI agent wrote it. To me, that isn't the most useful way to maintain control of a complex software project. ## Understanding the system matters more than inspecting every line When I work with coding agents, the important question isn't whether I've personally read every function they've produced. It's whether I understand the system I'm building. That means understanding its architecture, the technologies involved, how its components interact, the choices that affect maintainability, and the automated checks that tell me whether it behaves as expected. It also means knowing which parts of the system deserve closer attention. You can spend a lot of time reading individual implementations without developing a coherent understanding of the software as a whole. And if that understanding is missing, line-by-line review isn't going to replace it. I would rather spend that time making sure the architecture makes sense, that the project follows reasonable conventions, that important behavior is tested, and that the agents have the right information to work with. This isn't about trusting generated code blindly. It's about deciding **where human attention is most valuable**. ## Context management is part of keeping control One aspect I think is particularly underestimated is how AI agents manage and retain context. A coding agent needs more than the immediate instruction in its current conversation. It needs to know how the repository is organized, which decisions have already been made, what constraints the project has, what is currently being worked on, and why some approaches were chosen over others. Without that continuity, managing a complex codebase can quickly become messy. An agent might solve the task directly in front of it while missing an important constraint or working against an earlier architectural decision. That's also why I built [Syngraphe](https://syngraphe.dev/), an open-source tool for making repository context easier to structure, maintain, and check. The idea is to keep that knowledge in the repository itself, where humans and coding agents can work from the same context instead of relying entirely on what happens to fit in a conversation. I consider this a practical part of software engineering with agents, not an optional detail. Context management affects how well an agent can follow the direction of the project over time. ## Automated tests and fresh reviews are more useful than exhaustive manual inspection There is one practice I consider especially valuable: **periodically having a fresh AI session or a different agent review the codebase**. I don't mean asking the same agent to confirm that the code it just wrote looks fine. I mean giving another session or agent the opportunity to examine the implementation, architecture, and possible problems from a separate perspective. I would put this alongside a solid automated testing system as an important best practice for agent-driven development. Tests let you define behavior and check it repeatedly. Independent agent reviews give you another opportunity to identify issues, question decisions, and examine parts of the implementation that the original coding process may not have challenged sufficiently. Neither removes the need for human judgment. If a review surfaces something suspicious, or a test exposes behavior I don't understand, I still need to investigate. The point is that these mechanisms help direct that attention toward specific concerns rather than requiring me to inspect everything indiscriminately. For me, that's a more effective way to stay involved in the quality and direction of a codebase. ## You still need to understand the code when it matters None of this works if the person guiding the agents lacks the technical foundation to evaluate their decisions. I think one of the most important skills now is knowing how to direct coding agents while retaining the ability to understand the architecture and implementation whenever necessary. You need enough computer science knowledge to recognize the implications of different approaches and make informed decisions. That doesn't mean you need to be an expert in every technology an agent uses. If I'm working with something unfamiliar, I can ask the agent to explain the technology, the implementation, and the tradeoffs. I can then examine the relevant parts in more detail. Not remembering a particular language's syntax doesn't prevent me from understanding the underlying idea. But there is a difference between asking an agent to explain unfamiliar code and lacking the foundations needed to evaluate that explanation. Without those foundations, it's easy to accept decisions without understanding their implications. That becomes especially problematic as a system grows and has to remain maintainable. ## Computer science fundamentals may matter more, not less This leads to what I find the most interesting consequence of AI-assisted programming. **I think learning computer science fundamentals is becoming even more important as coding agents improve.** That sounds paradoxical. If agents can write more of the code, wouldn't programmers need to know less? I think the opposite may be true, at least when it comes to building and maintaining complex software. Syntax is becoming less central when you can delegate much of the implementation. What becomes more valuable is understanding how software actually works: memory management, data structures, execution, interfaces between components, performance, architectural tradeoffs, and the reasons behind design decisions. It's also why I think people learning to program today might benefit from studying lower-level languages such as **C or Rust**, perhaps even before starting with higher-level languages such as Python or TypeScript. I'm not suggesting they'll necessarily spend their careers writing C or Rust, or that higher-level languages are somehow less useful. My point is about what learning exposes you to. Lower-level programming can force you to engage more directly with the mechanics of a system rather than treating them as details hidden behind an abstraction. Those concepts can remain useful even when an AI agent handles most of the code you ship. The ability to produce syntax and the ability to reason about software are not the same thing. I think the second is becoming the more important differentiator. ## The job is to direct the system, not read every line I don't believe the right response to AI coding agents is to insist on personally inspecting everything they generate. I believe the better approach is to understand the system, establish sound architecture and engineering practices, test behavior properly, manage context deliberately, and use fresh sessions or independent agents to review the work periodically. Then inspect the code yourself wherever your judgment is needed. That requires real technical understanding, not less of it. For me, the goal isn't to become someone who can approve every line an agent writes. It's to be someone who understands **what is being built, how it works, and how to keep it reliable, efficient, and maintainable**. That's what maintaining control of an AI-developed codebase means to me.