10 Loops to Make AI Agents Smarter
goyalshaliniuk · x · 2026-07-18
The author outlines 10 loop designs to make AI agents smarter, arguing that instead of relying on a single prompt, system performance should be enhanced through "think-verify-refine-adapt" cycles.
Key loops highlighted include:
- Multi-Agent Collaboration Loop: Multiple agents dividing tasks like research, planning, writing, reviewing, and verification.
- Optimization Loop: Execute → measure → refine → repeat, used for continuous efficiency, accuracy, and cost improvements.
- Memory Loop: Retrieving context from past interactions and updating memory, ideal for assistants, customer service, and long-term tasks.
- Human-in-the-Loop: Outputting only after human approval, suited for healthcare, legal, and critical enterprise decisions.
- Confidence Loop: Providing an answer, evaluating confidence, and re-retrieving/refining when confidence is low.
The overarching takeaway: better loops are often more important than a single stronger model.
Related event: From Prompt Engineering to Loop Engineering(6 posts)→
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