How to Design a Loop That Prompts Your Agent
This article presents a loop architecture that enables an AI agent to autonomously complete multi-step tasks by building an automated prompting system instead of manually crafting each prompt. It breaks down the loop into five parts: defining a 'done' check, building prompts from dynamic state rather than hand-fed instructions, executing actions while capturing all outputs, feeding failures back as the next prompt, and setting hard stop conditions like max turns and cost limits. A walkthrough of fixing a login bug shows the loop in action, emphasizing that real costs come from repeated turns, making guardrails critical. Encapsulating repeated operations into reusable skills is highlighted as the key to long-term value, and common mistakes—like lacking an exit condition or discarding error output—are pointed out. Suitable for developers shifting from one-shot prompts to designing agent control flows.