Anyone else running into same problem deploying long-running AI agents?

I’ve been working on some AI projects recently — things like scheduled agents, API responders, and multi-agent systems that need to run continuously. One of the biggest headaches I’ve run into is deployment.

Most cloud platforms (AWS, GCP, etc.) are built for stateless apps or short-lived functions. But for long-running, stateful agents, the kind that need to persist data, auto-recover from crashes, and expose custom endpoints — it gets surprisingly messy. I’ve spent so much time setting up VMs, Docker configs, and recovery logic than actually writing agent behavior logic.

Has anyone else faced this?

Curious how others are handling deployment for autonomous agents that aren’t just scripts or jobs, but actual long-lived services. I’ve been working on a solution to make this easier, but before I share anything I’d love to hear how others are solving (or working around) this.