Agent sprawl is what happens when AI agents get built, deployed and connected across a company faster than anyone can inventory them, assign owners, control their permissions, watch what they do, or retire the ones that stopped being useful. Adoption runs ahead of governance.
Why is counting agents harder than counting apps?
An agent is cheap to create and easy to embed, so it arrives through a dozen doors: a low-code builder, a vendor feature switched on inside a product you already own, a script someone wrote. Each one was reasonable on its own, and none of them produced a row in a register anyone maintains. The remedy is an AI inventory treated as live operational data rather than a compliance artifact, which is also the only way non-human identity becomes governable.
What does agent sprawl cost before it causes an incident?
Duplicate agents doing the same job, spend nobody can attribute, and permissions that outlive the project. The expensive part is not the agents you know about, it is the ones still holding access to systems after their owner left. Fixing it is ordinary operations work: give every agent a defined agent lifecycle with a retirement step, and put the register under AI governance so someone answers for it.
Frequently asked questions
How is agent sprawl different from shadow AI?
Shadow AI is unsanctioned use: tools nobody approved. Agent sprawl includes everything you did approve, because approval was never the missing piece. Most sprawl is made of agents that were signed off individually and then never tracked as a population.
What is the first step out of it?
A current inventory with a named owner per agent. Not a policy, not a platform purchase: the list. Every later control, from permissions to retirement, needs something to attach to, and the common finding across adoption surveys is that most companies cannot produce that list on request.
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