can quickly generate a lot of code. Using the likes of Cursor or Claude Code, you’re able to rapidly develop powerful and capable applications. However, in many cases, the initial code these models generate is not fully production-ready.
There are a lot of quirks and imperfections in the code that could have serious implications if you try to deploy the code to production. Thus, you need to apply specific techniques to make sure that the code you generate with Claude Code is production-ready.
In this article, I’ll discuss how to make sure the code Claude Code generates is production-ready and ensures our business in the developed application.
This infographic highlights the main contents of this article. I’ll describe how to create production-ready code with Claude Code by first making sure we have initial robustness, and secondly, helping you iterate on the code, ultimately achieving production-ready code fully through autonomous coding agents. Image by Gemini.
Why generate code with Claude Code
First of all, we need to discuss why you should generate code with coding agents such as Claude Code. The main reason you should do it is simply that it saves you a lot of time. Writing code by hand is, of course, extremely slow. The next level of agentic coding is to have tab completion, where you start writing parts of code, and the AI completes it for you.
However, even time completion seems slow once you start developing with fully agentic systems. The reason for this is that you can simply describe what you want to build or what you want to achieve, and the coding agent can build it for you.
Of course, you gain less control over the actual code, but with the latest programming models, such as Claude Opus 4.6, the models are capable of producing code that matches what a human is able to write.
You should be generating code with coding agents simply because it saves you a lot of time and because the coding agents are capable enough to produce good code that will work well when deployed to production, given that you take the right precautions, which I’ll discuss in the next section.
How to make sure the code you generate is production-ready
There are two main aspects to making sure the code is production-ready. One is to provide the correct input and prompt to Claude Code when it does its first iteration of the code to make sure the code is as robust and production-ready as possible.
The second step is to have a review functionality where you look at the initially developed code, review it, and determine what needs to be fixed in order to deploy the code to production.
I’ll write one subsection for each step:
Improving robustness in the initial code
This step is important because this is where you generate the initial version of the code, and you, of course, want the generated code to be as good as possible. Now we need to ensure the initial code the agent generates is correct and production-ready, as production-ready as it can be. It’s also important to consider here that the initial code developed will heavily influence the architecture. It’s hard to completely change the architecture through iterations of the code. The outline for the architecture is usually done through the first iteration of the built code. If you want to change the architecture at a later point, you typically need to simply start from scratch, generating completely new code.
The main point of how I improve robustness and production readiness in initial code is:
- I have updated Claude.md and Agents.md files throughout my repositories, telling Claude Code how to generate code and what to watch out for
- I’m an avid user of plan mode, where I make sure to spend enough time planning with my coding agent before starting the implementation. This is important to make sure that the coding agent actually implements the application that I’m thinking of
- You provide clear instructions to the agent and make sure that you understand the problem you’re dealing with as well. And provide the agent with all the context it needs to make good decisions
For the point about Claude.md and Agents.md files, you can usually achieve this by always making sure to update those files whenever you change some code in the repository. So if you’re implementing a new feature or fixing a bug, you complete that task and then you make sure to tell the agent to generalize the knowledge from the thread where it implemented the feature slash fix the bug and note it down in the Claude.md or Agents.md files.
For the second point about using plan mode, this is incredibly important because you need to make sure that the agent understands your idea. Part of the challenge with writing natural language prompts instead of typing out code is that you’re less explicit in what you want to build. Of course, writing code is the ultimate level of explicitness because you’re literally writing logic. However, this is not efficient enough naturally, which is why we use natural language. However, when we use natural language, we again have to deal with the challenge of clearly stating what we want to build. This is where plan mode comes in handy because it allows the agent to ask you questions back as clarifying questions to make sure it understands the task at hand.
Lastly, it’s incredibly important to provide clear instructions to the agent, and you need to make sure that you understand the task yourself well enough. If you don’t understand the task well enough yourself, it’s very hard to explain to an agent how to efficiently solve the task as well. Furthermore, it’s incredibly important that the agent has enough context. If it needs to read issues on linear or messages in Slack, it’s important that you provide the agent access to this information so that it can make the best possible decisions itself.
Improving robustness through iteration
If you’ve gotten here, you already implemented the initial part of the code, and you now need to do some iterations to make sure the code is production-ready and working as intended.
I want to highlight two points that I do at this stage of my development. The first point is, of course, that I thoroughly test the implementation. I’m spending vastly more time, relatively, testing out the code that my agents have built compared to before. This is, of course, because the implementation part of coding has been commoditized by coding agents, while the testing part is still important for humans to take part in to make sure that the implementation is working as intended.
Thus, even though you feel like you spend a lot of time testing code, I usually think it’s worth it because the development process is still far more efficient than before. We’re simply spending more time testing the code because the implementation part has gotten so much more effective.
Secondly, I want to point out that I have a separate PR review skill setup on my computer. I can simply prompt my Claude Code to use the pull request review skill, and it will read through any PR or code that I’ve generated and make sure that it’s production-ready. What makes this skill even more powerful is that I’ve explicitly provided the skill with information about the repository where people have made mistakes before, and the code wasn’t working as intended.
This could be specific instances of people implementing something that went wrong when deployed to production, or specific precautions that have to be made when writing code in a specific repository.
Conclusion
In this article, I’ve discussed how to create production-ready code through Claude Code. I’ve covered two main points on how I make sure that the code I generate with my coding agents is production-ready. The first point is that I take care when I do the initial versions of my code by actively using Claude.md files, actively using plan mode, and providing clear instructions to my agent. Secondly, I improve code production readiness by iterating on the code through testing and a pull request review skill. This exposes a lot of issues, so I avoid deploying bugs to production. Going forward, I think having these tools that I’ve explained in this article in place to make sure that the code generated is production-ready will be incredibly important. There’s no way every developer has enough time to fully review pull requests, given the speed at which we’re developing code through coding agents. We do need to actively utilize coding agents to review code as well, and not just generate code.
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