You've built a working AI agent. It runs locally. It handles tool calls. It reasons through problems. You demo it to your team and everyone's impressed.
Then someone asks: "What happens when we deploy this to production?"
That's when reality hits: agents in production aren't like stateless web services. They're stateful systems that carry conversation history, tool results, intermediate reasoning, and context across multiple turns. If your container restarts—routine deployment, VM replacement, node upgrade—that entire session is gone. Your agent loses its memory. Work in progress disappears.
Add to that the operational complexity of teams: you can't let the marketing team's agent access the engineering team's secrets. You need isolation, access control, observability, and session persistence across infrastructure changes. But you don't want to rebuild all of that yourself.
This gap—between "working local agent" and "production-grade agent platform"—is exactly what most agent frameworks leave for you to solve.
