Programming with an AI Agent
Artificial intelligence (AI) has fundamentally changed how we program. AI agents can generate code, optimize it, and even assist with debugging. However, there are some limitations that programmers must keep in mind when working with AI.
Order and Duplication Issues
AI agents struggle with the correct ordering of code. For instance, they might place initializations at the end of a file, leading to runtime errors. Furthermore, AI can unhesitatingly define multiple versions of the same class or function within a project, causing conflicts and confusion.
A Code Platform with Memory and Project Structure Helps
One solution is to use AI code platforms capable of managing memory and project structures. This helps maintain consistency in complex projects. Unfortunately, these features are not always applied consistently. As a result, the AI might lose track of project coherence and introduce unwanted duplications or incorrect dependencies during programming.
Most AI coding platforms operate using 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 like Visual Code. Optionally, you can set up an LLM locally with Llama or Ollama and choose a MCP server to integrate with. Models can be found on Hugging Face.
IDE Extensions are Essential
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 on. 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 errors lies in how AI interprets APIs. AI models require context and a clear role description to generate effective code. This means prompts must be complete: they should not only include functional requirements but also explicitly state the expected outcome and boundary conditions. To facilitate this, you can store prompts in a standard format (MDC) and include them by default with the AI. This is particularly useful for generic programming rules you adhere to, as well as the functional and technical requirements and the structure of your project.
Tools like FAISS and LangChain Assist
Products such as FAISS and LangChain offer solutions to help AI better handle context. For example, FAISS assists in efficiently searching and retrieving relevant code snippets, while LangChain helps structure AI-generated code and maintain context within a larger project. However, you can also set this up locally using vector databases.
Conclusion: Useful, But Not Yet Autonomous
AI is a powerful tool for programmers and can help accelerate development processes. Nevertheless, it is not yet truly capable of independently designing and building a more 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 good result.
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