Deploying Agent Framework to Production: Azure AI Foundry, Observability, and Scaling

You've built agents. You've orchestrated workflows. You've integrated MCP tools. Now comes the part that separates demos from production systems: deployment. In Part 3 , we explored MCP and universal tool interoperability. This final part covers everything you need to run Agent Framework in production—Azure AI Foundry deployment, observability, session persistence, scaling, security, and cost management. The Production Gap Here's what usually happens: a developer builds an impressive agent demo, shows it to stakeholders, gets approval, and then... reality hits. "How do we debug when something goes wrong?" "Why did our Azure bill triple?" "The agent forgot the conversation after the server restarted." "It works, but it's slow under load." Production AI systems require the same rigor as any production software—plus new considerations around token costs, model latency, and non-deterministic behavior. Let's close that gap. Agent Framework in ASP.NET Core First, let's structure our agent ap