How AI coding agents work—and what to remember if you use them (arstechnica.com)

AI coding agents from leading companies like OpenAI, Anthropic, and Google have evolved to autonomously handle software projects over extended periods, encompassing tasks such as writing complete applications, running tests, and fixing bugs under human supervision. While these tools hold significant promise, they can also introduce complexities into software projects. By understanding the underlying mechanisms of these agents, developers can better determine when to utilize them and navigate the potential challenges. At the heart of these AI coding agents lies large language models (LLMs), sophisticated neural networks trained on extensive datasets that include both text and programming code. LLMs operate by recognizing patterns and generating outputs based on prompts, but this process can yield both useful logical inferences and errors. Recent advancements such as simulated reasoning models—which provide richer context for improving output accuracy—and multi-agent systems that connect various LLMs to work collaboratively, exemplify the ongoing refinement in the field. Each coding agent functions as a program wrapper that orchestrates multiple LLMs, with a supervising LLM managing task interpretation and execution, bolstering the potential to enhance productivity while mitigating pitfalls in software development.