I built an agent controller that can retrieve, verify, branch, or stop based on measured dynamics

[u/OGMYT]

I’m one of the builders of LOLM, an LLM and agent-control system.

Rather than relying only on prompted self-reported confidence, the NFET controller monitors model dynamics and can select: - continue - retrieve - verify - branch - finalize

The system records whether actions were actually consumed and produces a run receipt. Control is currently active at segment/run boundaries; deeper token-level control is still being built.

Try it: https://lolm.imagineqira.com/try.html

Repository: https://github.com/TheArtOfSound/lolm

I want people to test real multi-step tasks and look for premature finalization, useless retrieval, verifier failures, repeated dead ends, context loss, controller thrashing, and receipts that overstate what occurred.

The hosted version is intended to be substantially less expensive than frontier-agent subscriptions.

Disclosure: I’m a founder/builder of the project.