Programming with an AI Agent
Artificial intelligence (AI) has fundamentally changed the way we program. AI agents can generate code, optimize it and even help with debugging. However, there are some limitations programmers should keep in mind when working with AI.
Problems with order and duplication
AI agents struggle with the correct ordering of code. For example, they may place initializations at the end of a file, causing runtime errors. In addition, AI can, without hesitation, define multiple versions of the same class or function within a project, leading to conflicts and confusion.
A code platform with memory and project structure helps
One solution is to use AI code platforms that can manage memory and project structures. This helps maintain consistency in complex projects. Unfortunately, these features are not always applied consistently. As a result, the AI can lose track of a project’s cohesion and introduce unwanted duplications or incorrect dependencies while programming.
Most AI coding platforms work with so-called tools that the large language model can call. Those tools are based on an open standard protocol (MCP). It is therefore possible to connect an AI coding agent to an IDE such as Visual Studio Code. You can optionally set up an LLM locally with llama or ollama and choose an MCP server to integrate with. NetCare has a MCP server created to help with debugging and managing the underlying (Linux) system. Useful if you want to deploy the code live immediately.
Models can be found on huggingface.
IDE extensions are indispensable
To better manage AI-generated code, developers can use IDE extensions that monitor code correctness. Tools such as linters, type checkers and advanced code analysis tools help detect and correct errors early. They form an essential complement to AI-generated code to ensure quality and stability.
The cause of recurring errors: context and role in APIs
One of the main reasons AI agents continue to repeat mistakes lies in how AI APIs are interpreted. AI models need context and a clear role description to generate effective code. This means prompts must be complete: they should not only contain the functional requirements but also explicitly state the expected result and the boundary conditions. To facilitate this you can store prompts in a standard format (MDC) and include them by default with requests to the AI. This is especially useful for generic programming rules you follow and the functional and technical requirements and the structure of your project.
Tools like FAISS and LangChain help
Products such as FAISS and LangChain offer solutions to help AI better handle context. FAISS, for example, helps with efficient searching and retrieval of relevant code snippets, while LangChain helps structure AI-generated code and maintain context within a larger project. But here too you can optionally set it up locally with RAC databases.
Conclusion: useful, but not yet autonomous
AI is a powerful tool for programmers and can help accelerate development processes. However, it is not yet truly capable of independently designing and building a more complex codebase without human oversight. Programmers should regard AI as an assistant that can automate tasks and generate ideas, but still requires guidance and correction to achieve a good result.
Contact contact to help set up the development environment so teams can get the most out of it and focus more on requirements engineering and design than on debugging and writing code.
