Those deploying AI agents in large organizations — what use-cases are actually making it to production, and what's blocking the rest?

Been chatting with a bunch of folks across enterprises over the past few months and the AI agent space is moving fast. Some teams are planning to deploy hundreds, even thousands of agents — IT automation, customer-facing companion agents, internal workflow agents, you name it.

What's interesting is the split in how people are building them. Some are going the data platform route, extending their existing infrastructure. Others are building custom agent platforms from scratch. And there's a growing camp betting heavily on MCP architecture with tool-chaining and plugins. Each approach has its own trade-offs, but they all seem to converge on the same set of blockers once you try to move past the POC stage.

The three things that keep coming up in almost every conversation:

• Visibility: what agents do you actually have running, who spun them up, and what can they access? Shadow AI is becoming a real thing. Someone builds a cool agent with tool access in a hackathon, it works great, and suddenly it's in a production workflow with nobody tracking it.
• Access & behavior: once agents start calling APIs, executing code, or interacting with other agents, how do you know they're doing what they're supposed to? The gap between "it works in the demo" and "I trust this with production data" is massive.
• Continuous monitoring at scale: even if you solve visibility and access at deployment time, how do you keep monitoring all of this as agents evolve, models get updated, and new tools get added? This isn't a one-time audit problem, it's an ongoing one.

And honestly, what surprised me most is that these blockers seem pretty universal regardless of whether you're on the data platform path, custom platform, or MCP architecture. The underlying questions are the same: what do I have, what can it do, and is it behaving?

Curious if others are seeing the same patterns. Has anyone come across tooling or an approach for this that actually makes sense at scale? Most of what I've seen so far is either manual processes that won't scale or point solutions that only cover one piece of the puzzle.