How to scale agentic applications without creating AI sprawl

As agents move from answering questions to taking actions, they increasingly depend on a web of models, enterprise data, business semantics, tools, and applications. A single workflow might retrieve governed data, select a model, call several tools, hand off work to another agent, and update a business system — all while operating with the right permissions and leaving a sufficient trace to understand what happened.

As each team wires those pieces together independently, a new kind of AI sprawl can emerge: duplicated integrations, inconsistent policies, increased AI spend, fragmented context, and applications that become harder to change as the number of agents grows.