The way engineers interact with large language models is changing. Instead of crafting the perfect prompt and hoping for the best, a new methodology emerging from Anthropic’s ecosystem has developers building systems that prompt AI models repeatedly, letting the software observe its own output, adjust, and try again.
From prompts to loops
The shift started gaining traction after Anthropic published findings on December 19, 2024, explaining how agents could execute complex tasks using iterative cycles and environmental feedback. Rather than following static code paths, these agent loops allow models to dynamically control their own processes, observing results and course-correcting in real time.
Boris Cherny, a key figure in the development of Claude Code, captured the philosophical shift with characteristic bluntness.
“I don’t prompt Claude anymore. I have loops running that prompt Claude and figuring out what to do. My job is to write loops.”
