Summary of the best articles of Loop Engineering! After Agent began to focus on long tasks in 2026,

Summary of the best articles of Loop Engineering! After Agent began to focus on long tasks in 2026, the focus slowly became: How to design a loop system that can continuously think, execute, observe, verify and evolve? From Codex to Claude Code, from OpenHands to various Coding Agents. The biggest gap between side projects and production-grade systems is the Harness project, including Loop. Can the Agent continue to work for dozens of minutes or even hours? Is it possible to recover after a failure? Can costs be controlled? Can you tell when to stop? These problems ultimately fall on Loop design. 📚 Recommended reading

Loop Engineering — Addy Osmani
Loop Engineering — Firecrawl
What Is the AI Agent Loop? — Oracle
Harness Engineering — OpenAI
Harness Engineering for Coding Agent Users — Martin Fowler
Agentic Loops: From ReAct to Loop Engineering
Loop Engineering for AI Agents (Memory-First) — Mem0 📄 Recommended Papers
Agentic Harness Engineering
From Agent Loops to Structured Graphs 🛠 Recommended open source projects for research Codex CLI OpenHands PydanticAI OpenAI Agents SDK Focus on: How to run Loop How to stop Loop How to verify Loop How to recover Loop How to debug Prompt determines how the Agent starts. Context determines what the Agent can see. The Loop determines how far the Agent can ultimately go. Loop Engineering: Think ↓ Act ↓ Observe ↓ Verify ↓ Evolve ↓ Repeat You design the loop. Agents continue to improve in a loop. Each time it completes the cycle, the system gets closer to its goal than the last time. Agent never lacks loops. What’s missing is the engineering of the Loop.

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