Adding runtime guardrails to autonomous agents

[ishita-0301]

I've been experimenting with longer running agent workflows recently, and I noticed that many failures aren't caused by the model itself. Instead, they happen during execution.

Some examples I've run into:

• The agent gets stuck repeating the same action
• It keeps retrying a failing tool call
• It burns through tokens without making meaningful progress

While looking for solutions, I came across an open source project called FailproofAI that focuses on runtime reliability rather than orchestration. It provides mechanisms like loop detection and execution safeguards to help agents recover from these situations instead of endlessly continuing.

Repository: https://github.com/FailproofAI/failproofai

I thought this was an interesting complement to platforms like AstrBot. Curious if anyone here has dealt with similar failure modes in production and what approaches you've found effective.