Every company I talk to wants the same thing right now: let more people build with AI. Give product, operations, and finance teams the tools to spin up their own agents, and get engineering out of the way.
It’s a good goal. But it skips a question that someone eventually has to answer: who keeps all of those agents running?
The quiet cost of democratizing AI
The first few agents feel free. A team wires up a model, points it at some internal data, and ships something useful in a week. Then the second team does it, then the fifth. Each one picks its own model, its own prompt format, its own way of handling failures, or no way at all.
Somewhere along the line, the engineers who know how this works start getting pulled in. I know, because I’m usually one of them. Can you look at why our agent is hallucinating? Which model should we use? Is it okay to send this data to that vendor? Why did our bill triple?
None of those questions are on anyone’s roadmap. They get answered in hallway conversations and side threads, by whoever happens to know. That’s not a platform. It’s a help desk with no ticket queue.
This has happened before
We have seen this pattern several times. IT used to run everything. Then software delivery got fast enough that operations became its own discipline, and DevOps was born. Then companies moved to the cloud and ended up with hundreds of accounts nobody could fully list, and cloud infrastructure teams appeared to bring order to it.
AI agents are on the same path. The industry already has a name for it: agent sprawl. And the numbers suggest most companies are early in feeling it:
- Gartner reports that only 13% of organizations believe they have the right governance in place for AI agents.
- In a 2026 OutSystems survey of IT leaders, 94% said agent sprawl is increasing complexity, technical debt, and security risk. Only 12% had a central platform to manage it.
- Gartner also expects more than 40% of agentic AI projects to be cancelled by the end of 2027, because of rising costs, unclear value, or weak risk controls.
The pattern is consistent. Building agents is easy. Running them responsibly is not, and the cost shows up later, usually after something breaks.
What a small AI platform actually owns
The answer is not a big new department. At most companies it’s a small team, often a handful of engineers, that owns the shared pieces every agent needs:
- A gateway that every model call goes through, so cost, data access, and vendor choice are visible in one place.
- An evaluation framework, so “is this agent good enough to ship?” has an answer other than “it looked fine in the demo.”
- Monitoring, so failures and drift are caught by a dashboard instead of a customer.
- A registry of every agent in production, with a named owner, its risk level, and what it’s allowed to touch.
The goal of the platform is to make the safe way the easy way. Teams still build their own agents. They just don’t have to reinvent the plumbing, or guess at the rules.
Match the oversight to the risk
One idea I find useful: not every agent needs the same level of control. An agent that summarizes a report is not the same as one that issues refunds. Gartner’s recent guidance makes the same point, recommending oversight that scales with how much an agent can do on its own, from simply observing, to advising a person, to acting with approval, to acting alone.
Treating everything as high risk slows good work down. Treating everything as low risk is how you end up rolling agents back after an incident. A simple tiering, decided up front, avoids both.
Start before it hurts
The best time to set this up is when a company has two or three teams running agents in production, not twenty. At that point, a lightweight inventory, a shared gateway, and an evaluation template are a few weeks of work. After an incident, they’re a mandate, and a much more expensive one.
If your company is encouraging everyone to build with AI, ask one question this week: who owns the agents after they ship? If the answer is “whoever built it” or “engineering, kind of,” you’ve found the next platform team.
Sources: Gartner’s research on agentic AI project cancellations (June 2025), agent sprawl (April 2026), and proportional governance (May 2026); OutSystems, State of AI Development 2026. Figures are linked inline.