AI coding agents from OpenAI, Anthropic, and Google can collaborate on software projects for extended periods, writing applications, running tests, and fixing bugs under human supervision. They offer productivity boosts but are not magic, and missteps can complicate a project if expectations aren’t managed.
At their core, these agents rely on large language models (LLMs), a class of neural networks trained on enormous text datasets, including substantial amounts of programming code. LLMs are pattern-matching machines that use prompts to retrieve compressed representations from training data and generate plausible continuations. While they can interpolate across domains and problems, they can also produce confabulations when misapplied.
To improve usefulness and reliability, base models are refined with techniques like fine-tuning on curated examples and reinforcement learning from human feedback (RLHF). These steps nudge models to follow instructions, use external tools, and generate more actionable outputs.
Researchers have introduced innovations such as simulated reasoning, where a model expands the prompt with reasoning-style text to home in on better answers. Another breakthrough is the “agent” concept, which links several LLMs to perform tasks in parallel and then judge the results as a whole.
In practice, an AI coding agent functions as a wrapper around multiple LLMs. A supervising LLM interprets a user’s task and delegates subtasks to parallel LLMs that can interact with software tools. The supervising agent can pause, reassess subtasks, and steer the project, following a cycle described in Anthropic’s docs as: gather context, take action, verify work, repeat.
