Databricks makes AI agent search faster by teaching it when to stop

Databricks has expanded Adaptive Instructed-Retriever, a search model that dynamically decides how many retrieval rounds an AI agent needs instead of using the same search depth for every request. The company says the approach delivers comparable retrieval quality to leading models while responding about twice as fast, helping reduce latency and compute costs for multi-step enterprise searches.
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https://4sysops.com/archives/databricks-makes-ai-agent-search-faster-by-teaching-it-when-to-stop/