Runctl

Hey Product Hunt! I’m the builder behind RunCtl. I built RunCtl after repeatedly hitting the same wall: agents are easy to prototype, but painful to run in production. Every time I tried to ship an agent-based product for example I recently tried to ship an agent that does marketing, I ended up rebuilding the same infrastructure from scratch; task queues, retries, cost tracking, tenant isolation, and guardrails to prevent runaway spend. Agent frameworks make it easy to write agents, but they don’t help you operate them. RunCtl is a multi-tenant runtime and scheduler for AI agents. It handles agent lifecycle management, resource-aware scheduling, isolation, and observability so teams can deploy and scale agents without stitching together bespoke infra. This launch focuses on the core runtime: deploying agents, executing tasks, and seeing real usage and cost in one place. We’re early, but we’re building this alongside real users who want to run agents reliably in production. I’d love feedback from folks running agents today; especially around scheduling, cost controls, and what breaks first at scale. Happy to answer any questions!