Peter Steinberger proposes using automated orchestration loops to manage AI coding agents rather than direct prompting
Matthew Berman demonstrated the concept using GitHub-triggered automation loops
Positive users praise explanations of AI coding agent loops as insightful and useful, while negative users dismiss the articles as AI-generated slop and view the loops as wasteful.
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wtf is a loop?
Here’s your monthly reminder that you shouldn’t be prompting coding agents anymore.
You should be designing loops that prompt your agents.

@mvanhorn https://youtu.be/mR-WAvEPRwE?t=1042&si=9ovo730xQTK3Y0cz
Run long running agents.
imagine loops with Mythos...
wtf is a loop?
@steipete I made a video explaining loops:
wtf is a loop?

@mvanhorn @garrytan Great read, very helpful!
With where I'm at, I zero in on this:
Reposting the banger quote from @tomas_hk “You won’t need to be a power user to spend a shit ton of money on AI. You will just need your AI agent to be a power user to spend a shit ton on AI."
Loops
wtf is a loop?

@mvanhorn Double Loop :)

@mvanhorn Worth clarifying: loops don't replace prompts. It just determines what the prompt has to prove.
I find this quite helpful

@mvanhorn If I’d wanted to read an AI’s opinion on the matter, I would have just prompted an LLM myself.

@OranAITech Eyes 👀

@mvanhorn The loop is only as good as its memory.
State that evaporates between runs doesn't compound. Verifiable, persistent storage is what turns a loop into something that learns over time.

@dsiroky Agreed. But Peter says he never writes plans!

@mvanhorn Love your content but this is so obviously written by claude it’s painful

@mvanhorn @garrytan I knew you were going to write this! 😅

@mvanhorn TLDR; "A loop is a small program that prompts the coding agent for you, reads what it produced, decides whether it is done, and if not, prompts it again."
Just a heads-up: run this on a subscription, not API tokens - unless you enjoy financial self-harm

@mvanhorn The observability, auditing, and guardrails piece is what will make this practice actually work. We created a project (http://github.com/agentspan-ai/agentspan) to address this in a way where you just define a few python methods and pass in the model.

Scrubbed through this a bit and I think you're in the right direction, but I think what Peter and Boris were talking about the outer loop.
/goal or /loop aka "ralph loops" would be the inner loop. The inner loop takes a task or end state and just prods the model to keep going until it fully completes the task. This takes care of models just kinda being lazy or not fully finishing the task or just generally trying to be done with their agent turn.
The outer loop, or the "loop that prompts claude" is more akin to your automation setup. The outer loop in this example is an agent that watches the github issues, and spins up another agent/thread with its own goal to triage, validate, fix, and respond to the issue.
To implement features, the outer loop would play the role of the human previously. Instead of the human looking at github issues. The agent would handle it, then spin up a thread/subagent with its own goal to handle that one specific issue.
The outer loop is the start of the composition of these loops. That's what they mean. They're beyond automating features at this point. They're automating systems.

@mvanhorn You can pretty much do that by designing a plan in grok build or codex and then /goal it so it doesn’t stop before it’s finished. Needs a decent plan

@mvanhorn @GeoffreyHuntley ^

@mvanhorn If I’d wanted to read an AI’s opinion on the matter, I would have just prompted an LLM myself.