Programming with an AI Agent
Artificial intelligence (AI) has fundamentally changed the way we program. AI agents can generate code, optimize it, and even assist with debugging. However, there are some limitations that programmers should keep in mind when working with AI.
Issues with sequence and duplication
AI agents struggle with the correct order of code. For example, they may place initializations at the end of a file, which causes runtime errors. Additionally, AI can define multiple versions of the same class or function within a project without hesitation, leading to conflicts and confusion.
A code platform with memory and project structure helps
A solution to this is using AI coding 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 may lose the coherence of a project and introduce unwanted duplications or incorrect dependencies during programming.
Most AI coding platforms work with so-called tools that the large language model can invoke. These 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 created a MCP server to help with debugging and managing the underlying (Linux) system. Useful if you want to deploy code live immediately.
Models can be found on Hugging Face.
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 to detect and correct errors early. They form an essential supplement to AI-generated code to ensure quality and stability.
The cause of repetitive errors: context and role in APIs
One of the main reasons why AI agents continue to repeat errors lies in how they interpret APIs. AI models need context and a clear role description to generate effective code. This means that prompts must be complete: they should not only contain the functional requirements but also explicitly state the expected result and constraints. To facilitate this, you can save prompts in a standard format (MDC) and include them by default with the AI. This is especially useful for generic programming rules you follow, as well as functional and technical requirements and your project structure.
Tools like FAISS and LangChain help
Products such as FAISS and LangChain offer solutions to help AI handle context better. FAISS, for example, helps with efficiently searching and retrieving relevant code fragments, while LangChain helps with structuring AI-generated code and maintaining context within a larger project. But here too, you can optionally set it up locally yourself with RAG 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 complex codebase without human oversight. Programmers should view AI as an assistant that can automate tasks and generate ideas, but which still requires guidance and correction to achieve a high-quality result.
Contact contact to help set up the development environment, assist teams in getting the most out of their development environment, and focus more on requirements engineering and design rather than debugging and writing code.
