Great explanation. I think one of the biggest misconceptions is that an Agent Gateway is simply an API gateway for AI. In reality, it becomes the governance and control plane for enterprise AI.
As organizations adopt multiple models and AI agents, the challenge shifts from "Which model should I use?" to "How do I consistently govern all of them?" That's where an Agent Gateway adds value. Providing centralized authentication, policy enforcement, model routing, token usage visibility, audit logging, and failover, regardless of whether the inference is served from a public provider or a self-hosted model.
From my perspective, this also decouples applications from individual model providers. Teams can evaluate, replace, or upgrade models without rewriting application logic or rebuilding governance controls, which is a significant advantage as the AI ecosystem evolves.
I built an mcp-api gateway for this purpose. Allow my AI and Agents to manage Nutanix. I only run CE locally for testing but my goal was to unify network security and connectivity between Flow and bare-metal OVS and Cillium with natural language. Still working on some of the Nutanix specifics. Adding 3rd party switching tests soon. I wonder how people are using AI to make Day 2/3 easier?