How AI coding agents work-and what to remember if you use them

"AI coding agents from OpenAI, Anthropic, and Google can now work on software projects for hours at a time, writing complete apps, running tests, and fixing bugs with human supervision. But these tools are not magic and can complicate rather than simplify a software project. Understanding how they work under the hood can help developers know when (and if) to use them, while avoiding common pitfalls."

"We'll start with the basics: At the core of every AI coding agent is a technology called a large language model (LLM), which is a type of neural network trained on vast amounts of text data, including lots of programming code. It's a pattern-matching machine that uses a prompt to "extract" compressed statistical representations of data it saw during training and provide a plausible continuation of that pattern as an output."

"These base models are then further refined through techniques like fine-tuning on curated examples and reinforcement learning from human feedback (RLHF), which shape the model to follow instructions, use tools, and produce more useful outputs. Over the past few years, AI researchers have been probing LLMs' deficiencies and finding ways to work around them. One recent innovation was the simulated reasoning model, which generates context (extending the prompt) in the form of reasoning-style text"

AI coding agents rely on large language models trained on vast text and code corpora and operate as pattern-matching systems that produce plausible continuations from prompts. These models compress training data into statistical representations that enable interpolation across domains and occasional confabulation errors. Base models receive further refinement via fine-tuning on curated examples and reinforcement learning from human feedback (RLHF) to follow instructions and use tools. Innovations like simulated reasoning add contextual reasoning-style text to improve outputs. Agents often link multiple LLMs together in program wrappers to perform tasks, evaluate results, and extend capabilities, while human supervision remains necessary.

Read at Ars Technica