An AI agent coding skeptic tries AI agent coding, in excessive detail (minimaxir.com)

An experienced data scientist, initially skeptical of AI agents for coding, recently detailed their experiment with the latest generative models, specifically Anthropic’s Claude Opus 4.5. After documenting concerns about unpredictability and subpar performance in previous agents, they set out to rigorously assess Opus 4.5's capabilities by integrating it with their coding projects. Utilizing an AGENTS.md file for structured prompting, they found that the AI significantly enhanced productivity by fixing Python code, improving readability, and implementing complex features with minimal errors. This marked a notable shift from their earlier experiences with less effective models. The significance of this exploration lies in the potential of LLMs, particularly Opus 4.5, to aid in programming tasks beyond basic suggestions, fostering an environment for more complex coding challenges. The hands-on approach with detailed prompts and the structured AGENTS.md not only yielded robust and efficient code, but also showcased an evolution in LLM capabilities, igniting optimism in the AI/ML community about the practical applications of coding agents. This case emphasizes the growing sophistication of AI in real-world scenarios and invites further experimentation with challenging coding tasks.