@Vtrivedy10: another banger from Sydney! i think this whole hierarchy of loops is still super early but some primitives we know work…
Summary
The tweet discusses the early stage of hierarchy of loops in AI agents, highlighting verification as a key primitive for reliable semi-long-horizon work.
View Cached Full Text
Cached at: 06/16/26, 11:41 PM
another banger from Sydney! i think this whole hierarchy of loops is still super early but some primitives we know work
ex: verification as a primitive is so ridiculously important for non-slop semi-long-horizon work, it’s worth spending days to weeks making sure the distribution of outcomes you want from your agent are verifiable in practice by your system
Similar Articles
@sydneyrunkle: https://x.com/sydneyrunkle/status/2066928783534289358
This blog post by Sydney Runkle explains the art of loop engineering for building reliable LLM agents using LangChain primitives, covering four levels of loops: agent loop, verification loop, event-driven loop, and hill climbing loop.
@swyx: ## On Loopcraft One might argue the entire game of the next century is to be able to stack loops as effectively as poss…
A conceptual discussion on the importance of stacking loops in AI agent design, drawing parallels to Sutton's Bitter Lesson and advocating for scalable systems over manual fixes.
@bibryam: Practical Loop Engineering https://addyo.substack.com/p/practical-loop-engineering… @addyosmani covers goals, loops, an…
Addy Osmani discusses practical loop engineering for AI agents, covering goals, autonomous feedback cycles, and the use of tools like Claude Code and Codex for managing parallel agent tasks.
@sydneyrunkle: interesting point here: loops amplify behavior, making them a double edged sword but we know loops are the future, so h…
Discussion on how loops in AI agents can amplify both good and bad behaviors, emphasizing the need for an engaged human in the loop to guide the agent's learning of user preferences.
@akshay_pachaar: https://x.com/akshay_pachaar/status/2069118430582866051
This article explains the concept of loop engineering in AI agents, emphasizing that the core loop is trivial but the critical work lies in the harness around the model, including knowing when to stop and preventing context rot.