Why Running AI Agents in Production Is Harder Than Running a Web App
Most of us carry a mental model for production infrastructure that took years to build and mostly still works: stateless containers behind a load balancer, autoscaling on CPU or queue depth, a CI/CD pipeline that ships a new image, health checks that restart what dies.
It's a good model.
It's also the wrong model for a meaningful chunk of what an AI agent needs once it stops being a demo and starts running unattended, handling real workloads, for real users, continuously.
The temptation is to treat an agent deployment like "a web app that happens to call an LLM." That framing survives for about a week in production before it starts producing incidents that don't map cleanly onto anything in the standard web application runbook.
This happens because a handful of assumptions baked into the web app model quietly stop being true for deployed AI agents, and the operational moves...
Copyright of this story solely belongs to hackernoon.com. To see the full text click HERE
