On Making Machine-Generated Code Production Ready
I have been reluctant in using coding agents as part of my process, because it felt unnatural and unauthentic. Especially because I knew the way they work and the unwarranted hype around it made it even more difficult to choose to be part of my coding exercises. For me authoring a program was a sacred ritual, and so I have been blind to the things that these coding assistants and agents solve i.e. making stuff that is useful not just technically pure and stimulating[f1].
But coding agents today can generate astonishing amounts of code in a very short time. One can describe a system in natural language and watch a working implementation appear within minutes. This is a dramatic shift from the traditional pace of programming, where writing even a modest application required hours or days of careful manual effort. Personally for some projects, I take months or years before I actually sit down and write the actual code, not because those are hard problems though (some of them are) but my laziness and wanting of the best ideas to take shape before they are manifested as code.
However, speed at which the code is generated should not be mistaken for readiness. The code produced in the first pass by these systems is rarely suitable as-is. It often contains small imperfections, architectural shortcuts, or assumptions that may not hold in real scenarios. If left unchecked these can fail miserably. Therefore even with the help of these agents the challenge is not merely generating code, but shaping that generated code into something robust, predictable, and safe to use.
In this note I describe a way of thinking about this process. It consists of two phases. First, shaping the initial generation so that the architecture begins on a stable footing. Second, iterating carefully until the code reaches the level of reliability expected for actual use. This is not the only mental model and the workflow I stay in during the development process, but this is a close approximation of what someone would need to get a hold of what is going on and how to direct it for fruitful results.
Why Generate Code With Agents
The first question one might ask is simple:
Why generate code this way at all?
The answer is speed.
Writing software entirely by hand is slow. Even when one relies on fantastic editing environments like Emacs, the process still requires the programmer to construct most of the logic idea by idea, function by function and obviously line by line. Agent-assisted coding changes this process, by letting us describe the intention of the program that lets the system produce an initial implementation, instead of composing the implementation directly. This dramatically compresses the time between idea and working prototype.
There is, of course, a tradeoff, a big one. We give up some immediate control over the exact form of the code, and there is that old purist in me nagging me all the way about losing control. But the modern generation systems are capable enough that their first output often resembles what a competent engineer might write. When handled carefully, the generated code can and usually serve as a strong starting point. Though I should warn you that this depends on the programming language you choose. Popular languages are well supported, but if you’re like me you sometimes want to work with a not so popular language, the experience may not be that smooth.
The point is that generation saves time, but only if one applies discipline afterward. Without that discipline the speed of generation simply produces fragile systems faster. Trust me on this.
Ensuring the code is not fragile
Turning generated code into production code has two major parts.
i) the initial generation must be guided carefully. The structure produced in the first iteration strongly influences the architecture of the entire system.
ii) the code must undergo systematic iteration and review. This stage is where the majority of reliability improvements occur.
Second one is equally important, if not more.
Improving robustness in the first cut implmentation
The first version of the system determines the skeleton on which everything else is built. Once the structure is established it becomes difficult to alter it significantly through small edits. Large architectural changes often require starting again from scratch
For this reason, the early stage of generation deserves deliberate attention.
Large architectural changes often require starting again from scratch, and I do often restart from scratch. It is just easier than messing with bad ideas in current code base because we couldn’t see in the beginning what we can see now after the fact.
This is an important lesson for me personally, because the reason it took me days or months for certain projects was, that I couldn’t see all the pieces and problems of the project in the beginning. By iterating quickly, my brain was able to connect these readily, which was very hard if at all possible (if you don’t write them down, you tend to forget). Moreover having your ideas in executable form such as code, is much more useful to test out idea rather than keeping them all in your head. Now other people can also collaborate and help you out.
One useful technique is to maintain clear documentation inside the repository describing how code should be written. These documents act as guidance for future generations of code. They capture conventions, constraints, and lessons learned from earlier work.
Whenever a new feature is implemented or a bug is fixed, it is worth reflecting on whether the knowledge gained should be recorded.
This may feel like overkill, or even a burden. When you feel like that, tell yourself, “Come on. That is the least you can do. You already offloaded writing code. You have to make sure that you know what is in the code and why those changes were necessary”
Over time this produces a body of memory that improves the quality of subsequent code generation.
Planning also becomes extremely important in this environment. When programming manually, the act of writing code forces one to think through the logic step by step. When generating code through natural language, the description can easily become vague. Spending time clarifying the intent of the system before implementation helps ensure that the generated code reflects the actual design.
Clear instructions are essential. The system generating the code can only act on the context it is given. If the problem itself is poorly understood, no amount of generation will produce a reliable solution. The programmer must first understand the task deeply enough to describe it precisely.
Context matters as well. Design decisions are rarely made in isolation. Historical discussions, previous issues, operational constraints, and architectural documents all influence the correct implementation. Supplying this information ensures that the generated code fits the larger system rather than existing as an isolated fragment.
Improving Robustness Through Iteration
After the first version of the code is produced, the real work begins.
The most important activity at this stage is testing. In fact, the balance of effort shifts noticeably. Because the implementation is generated so quickly, a larger portion of development time is now spent verifying that the system behaves correctly.
Testing becomes the primary tool for discovering unknown and hidden assumptions, incomplete logic, and other subtle problems. Even though this stage may appear time-consuming, the overall process remains far faster than traditional methods.
Another useful practice is systematic review. Generated code benefits greatly from an additional layer of analysis that examines the implementation as a whole. Such reviews can check for common failure modes, architectural inconsistencies, or patterns that have caused trouble in the past.
Once you generate few hundred lines of code, do a systematic review on the whole code base.
Over time, these reviews can be informed by accumulated experience. Previous mistakes, or architectural pitfalls can be codified into review guidelines. This allows the review process to catch problems that might otherwise escape notice.
This result in a feedback loop generation produces the initial implementation, review identifies weaknesses, and iteration gradually strengthens the system.
Conclusion
The ability to generate large amounts of code quickly is one of the most significant shifts in modern software development. Yet the value of this capability depends on how it is used.
Production-ready systems do not emerge automatically from code generation. They are the result of careful guidance during the initial generation and thoughtful iteration afterward. Documentation, planning, clear problem descriptions, testing, and systematic review all play a crucial role.
The future of programming will likely involve these systems not only as code generators but also as collaborators in the review and refinement process. As development speeds increase, it becomes increasingly difficult for a single engineer to manually examine every line of code. Tools that help analyze and critique generated implementations will therefore become just as important as those that produce them[f2].
In the end, the goal remains the same as it has always been: building software that works reliably in the real world.
- Richard P. Gabriel, Worse Is Better. https://dreamsongs.com/WorseIsBetter.html
- I still love the old school way of authoring programs by hand, but it is what it is. The utility of these assistants cannot be ignored.