How to scale agentic applications without creating AI sprawl

Building an agent is getting easier. More capable models and coding agents are making it faster to build and iterate. Operating many agents across an enterprise, however, is a different problem.

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.

The challenge is scaling those applications without multiplying the infrastructure around each one.

We recently brought together Databricks, OpenAI, and Stellantis to examine what it takes to scale agentic applications in production. A central theme was that as agents become more capable and autonomous, the infrastructure around them matters more.