The original title is "Write Loops, Not Prompts: Why AI Agents Work Better When They Iterate"

Short summary

The article explains why agent loops—where an LLM iterates with tool calls until a task completes—outperform single-shot prompting for multi-step work, citing OpenAI's Codex agent loop and Philip Zeyliger's 9-line Python implementation at Sketch.dev. Loops give models room to fail, self-correct, and accumulate context across iterations, enabling automation of complex tasks like merge conflict resolution and code review. The latter portion pivots into promoting Octo, a workspace product that wraps agent loops with human review, persistent agent identities, and multi-agent orchestration patterns.

• •Agent loops (LLM iterates with tool calls until done) solve multi-step problems that single prompts cannot
• •OpenAI's Codex and Sketch.dev's implementation show loops enable self-correction and context accumulation across hundreds of tool calls
• •Octo product pitch: wraps agent loops with human-in-the-loop review, persistent agent identities, and multi-agent orchestration modes

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