What Happens When the Research Loop Gets Stuck?

[ishita-0301]

This project got me thinking about a different side of autonomous loops. In longer running agent workflows I often see failures that have nothing to do with the metric itself. Things like the agent getting stuck repeating the same action, endlessly retrying a bad path, or spiraling because of a tool error.

I've been experimenting with an open source project called FailproofAI that focuses on those runtime failure modes rather than the optimization loop itself. It adds safeguards like loop detection and recovery so the agent can fail more gracefully instead of wasting hours on a dead end. Repo here if anyone wants to take a look: https://github.com/FailproofAI/failproofai.

Feels like combining a strong evaluation loop like "autoresearch" with runtime guardrails could make autonomous systems much more robust in practice.

[vlasenkoalexey]

See if some ideas from https://vlasenkoalexey.github.io/2026/06/making-karpathy-autoresearch-production-ready/ can help