I tried making a “minimum inventory” for an AI agent fleet — what am I missing?

I've been trying to understand the agent-sprawl problem beyond the usual “agents need governance” discussion. So I tried to reduce it to something very basic: If a company has multiple (say more than 10) agents running across different teams, what should they be able to answer about every single one? My first pass was: Who owns it? What is it actually allowed to do? Which model is it using? What data/tools can it access? Where is it running? What version is deployed? How much is it costing? When was it last evaluated? Can I see what it actually did? The interesting part is that none of this feels particularly “AI-specific” anymore. It starts looking a lot like inventory+access control+observability+deployment management. And I suspect the problem gets ugly once you have agents spread across different frameworks, coding tools and cloud environments. I made a simple visual of the checklist because I'm curious whether I'm missing something obvious. For people actually running agent fleets: what are the 2–3 fields you absolutely need that aren't on this list? I've come across a few platforms trying to tackle different parts of this: Lyzr's Control Plane, Fiddler's AI Control Plane, SailPoint's Agentic Fabric, TrueFoundry, and some of vendor-native stacks from the hyperscalers. But I'm much more interested in the underlying checklist than the tools. Are these platforms actually solving the fleet-management problem, or are we still mostly stitching together observability + IAM + CI/CD + security ourselves?