“The problem with agentic AI at scale is that consumption is hard to forecast, agents searching and re-searching create compounding, unpredictable cost and latency, and finance teams hate these variable bills. Knowing your agents will search within a defined ceiling, and that you can set that ceiling per workload, is what makes agentic search safe to run at scale rather than a runaway meter,” Chaturvedi noted.
The economics could become even more compelling, Chaturvedi added, if the specialized model can deliver the claimed retrieval quality of larger general-purpose models with lower latency.
Adaptive Instructed-Retriever, according to Databricks, matched or exceeded the retrieval quality of Claude Sonnet 5, GPT-5.6 Luna and DeepSeek-V4-Flash in its internal evaluations while completing requests in 5.8 seconds, or more than twice as fast as those models.
