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.
Official announcementDatabricks Blog
How to scale agentic applications without creating AI sprawl
From the official release
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.
This is a short excerpt. Read the full announcement on the official source.
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