15:I[981,["658","static/chunks/658-a698693f56d0116a.js","183","static/chunks/183-6beeeb801c274aaa.js","474","static/chunks/app/%5Btype%5D/layout-631c4730b83c8223.js"],"Posts"] 16:T475,My minimal LLM Agent (GPT4.1) just wrote it's own Agent Loop function.

Here is the backstory. I'm writing a tutorial: Zero to LLM Agent. Right now it is past 2 am here. I just wrote my 6th post. In the first five posts I wrote code that can talk to the OpenAI LLM. Then I gave it exactly one tool: a Python interactive environment (basically a Python REPL).

But, I used ipython and never actually implemented an agent loop. I defined an infer function infer(context, toolkit, evaluator) -> context, where context is just a simple list. The infer function then does the work. It takes the context, gives it to the LLM API, and performs the toolcall if needed.

Then I told the LLM Agent "The CWD is a Python project of an LLM Agent. Ignore the md file. Look only at the PY files in the CWD. Determine what is missing to make an LLM agent."

With a few more instructions by me, it implemented a perfectly reasonable agent loop function. The LLM Agent just improved it's own code.

Are we at a point where a bootstrap agent code can and will improve itself?

I would love to hear your thoughts on the matter.17:T47d,Over the last weeks I’ve been experimenting with a very narrow problem in the agent ecosystem:

We have marketplaces, orchestration frameworks, SDKs, and payment rails, but no minimal, framework-agnostic execution surface that lets heterogeneous agents actually coordinate work in a predictable way.

Most current setups tightly couple: - Execution logic - Economic logic - Framework internals

This makes interoperability brittle.

So I built a small experimental reference implementation that focuses on: - A middleware interception layer for tool-call validation - A minimal A2A JSON-RPC draft spec - An “economic hook” abstraction that can sit beneath orchestration or marketplace layers

Framework-a

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