As we evolve agents in real enterprise contexts — across systems, processes, and decision boundaries — one risk keeps surfacing: most enterprises are building agentic AI the way they once built shadow IT: fast, smart, and siloed. The models are moving, the stickiness is real, and knowledge workers are already adapting. But the bottleneck isn't capability.
What's missing is an operating model.
Isolated agent development is everywhere right now — good intent, impressive demos. But point tools stay point solutions. The mental model that makes more sense to me is agents as a managed workforce: clear roles, ownership, guardrails, access boundaries, and a way to measure reliability over time.
That also means building what I'd call a "context normalizer" — a translation layer between how the organization actually works and how agents behave inside it. Without it, you get agents operating inconsistently, escalating decisions they should own, or running without policy boundaries in sensitive domains. We don't expand human scope or autonomy without clarity on expectations and observed performance. Agents should be no different.
This is still an open-ended space — and the VM sprawl parallel feels apt to me: easy to spin up, hard to govern, and messy to unwind without a clear operating model. If agentic AI is going to scale in enterprise context, it will need the same discipline we apply to people and teams: a repeatable way to onboard, evaluate, and grow agent capability. Otherwise there'll be a lot of activity, and a fragmented end state.
First published on LinkedIn, March 15, 2026.