Agent Loops Outperform Single Prompts by Letting AI Iterate and Self-Correct

Most LLM users rely on single-prompt exchanges, but agent loops — where a model repeatedly calls tools, receives results, and reasons over accumulated context — handle complex, multi-step tasks far more effectively. OpenAI detailed this architecture in a January technical post on Codex CLI, describing how one conversation turn can involve hundreds of tool calls, each enriching the model's working context. Developer Philip Zeyliger independently demonstrated the approach at Sketch.dev, showing that just 9 lines of Python implementing a while-loop could automate tasks like merge conflict resolution and error chain debugging. Because each tool call output is appended to the prompt, the model builds a running memory of past attempts, allowing it to detect failures and adjust without human intervention. While loop-based agents enable parallelisation and reduce processing time, they also carry costs such as growing prompt sizes, which OpenAI addresses through techniques like prompt caching.