Built an open-source runtime layer to stop AI agents before they overspend or take risky actions — looking for feedback

If you’re experimenting with AI agents, you’ve probably run into this problem: once an agent starts calling tools, APIs, models, email systems, databases, or jobs, it can become hard to control what happens next. Permissions answer: “Can this agent use this tool at all?” Rate limits answer: “How fast can it call it?” But agents fail in a different way. They retry, loop, fan out, call expensive models, send too many emails, trigger jobs or keep acting after the run has already gone off track. I built Cycles to tackle this problem. It’s an open-source runtime authority layer for AI agents.