Loop Engineering: Building Reliable AI Agents Through Stacked Loops and LangChain Instrumentation
By
Sydney RunkleJune 16, 20267min
Summary
This article explores the concept of "loop engineering" in building reliable AI agents. It explains that while the core agent algorithm is simple — giving an LLM context and letting it call tools in a loop — effective agents require carefully designed harnesses with multiple stacked and extended loops. The piece references Swyx's concept of "loopcraft" and discusses how to instrument each level of these loops using LangChain primitives to build more dependable, task-specific agents.
Source
Twitter / XLoop Engineering: Building Reliable AI Agents Through Stacked Loops and LangChain Instrumentationlangchain.comKey quotes
· 3 pulledThe core agent algorithm is simple: give the LLM context and let it call tools in a loop until it's done.
Getting agents to do valuable work reliably takes more than just a good model: it requires a carefully designed harness that's fit to a set of tasks.
Swyx recently wrote a great piece on 'loopcraft: the art of stacking loops', the idea that you can stack and extend loops to build more effective agents.
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