Built an open-source runtime layer to stop AI agents before they overspend or take risky actions — looking for feedback
Imagine an AI agent about to send a flood of emails or blast through a budget — then suddenly, it gets stopped in its tracks. That’s exactly what /u/jkoolcloud built with Cycles, an open-source runtime layer designed to pause risky or costly actions before they happen. Instead of waiting for the damage or expense to be done, Cycles checks whether an action fits within the allowed limits — be it budget, policies, or safety thresholds. If it does, it reserves the allowance, executes the action, and then records what actually took place. If not, it blocks the move entirely. This approach aims to control runaway loops, API overuse, or multi-step workflows sharing resources, without replacing existing observability tools. As /u/jkoolcloud explains, this pre-execution guardrail is about catching problems early, before costs pile up or mistakes happen. And get this — lots of folks are wondering if this reserve-then-act model could become a new default for safer AI systems. That shift is subtle now, but it’s exactly the kind of signal that usually defines the next cycle.