Databricks adds adaptive search model to speed agent retrieval
Databricks Inc. today expanded its Adaptive Instructed-Retriever search model to speed up response times for requests from artificial intelligence agents that require multiple rounds of retrieval.
The company said the model is a retrieval building block for its Genie Code, Genie One and Genie Agents that matches the retrieval quality of several leading third-party and open-source models while responding twice as fast as Anthropic PBC’s Claude Sonnet 5, OpenAI Group PBC’s GPT-5.6 Luna and Hangzhou DeepSeek Artificial Intelligence Co. Ltd.’s V4-Flash. The results came from a mix of seven held-out internal and external benchmarks spanning different domains and levels of search difficulty.
Adaptive Instructed-Retriever builds on Instructed-Retriever-1, a model released earlier this year that sought to improve on conventional retrieval-augmented generation by carrying user instructions, examples and data-source schemas through the retrieval and response-generation process. Databricks previously reported that the architecture improved performance by more than 70% over traditional RAG on a suite of enterprise question-answering tests.
