Really excited to share our latest research on the Instructed Retriever - a novel retrieval architecture that reimagines search for the agentic era. databricks.com/blog/instruct… Amazing work by @cindyxinyiwang and @mrdrozdov who co-led this effort!

Most agentic search systems get better by thinking longer: more tool calls, more reason-act loops, each step waiting on the last. Quality goes up, but so does latency. Instructed-Retriever-1 takes a different route. Instead of scaling test-time compute sequentially, it scales it in parallel. One retrieval-specialized model fans the work out: it generates multiple query and filter formulations to widen recall, then reranks the merged evidence with a multi-pivot reranker to sharpen precision. Both stages run at once, so searching more broadly no longer means searching more slowly. The result inside Knowledge Assistant: search time drops more than 3x and answer time 2x, with time to first token around two seconds, and no drop in quality (it matches Claude Sonnet 4.5 retrieval quality on KARLBench). For the people using it, that means far less waiting between question and answer, the freedom to ask more follow-ups, and more of the knowledge base actually surfaced. Rolling out to all customers now, with no reconfiguration. Read how we did it: databricks.com/blog/3x-faste…

Really exciting new collaboration with @NegarEmpr @mrdrozdov and @matei_zaharia on query utility gap between ranking and generation (to appear #SIGIR2026). Check it out!

Most enterprise questions don't live in one dataset. They span structured systems and unstructured sources like documents, reviews, and reports. In our latest research, we show how Agent Bricks Supervisor Agent handles this by decomposing queries across structured and unstructured tools, then synthesizing results over multiple reasoning steps. The results across STaRK and KARLBench: 20% improvement over SoTA baselines, with the biggest gains on tasks requiring tight integration of structured and unstructured data. All built declaratively — no custom code, just precise instructions and the right tools. databricks.com/blog/agentic-…

Applications are officially open for the Grounded Reasoning Cup at Data AI Summit 2026! 🏆 We’re looking for students who want to: - Tackle high‑impact enterprise challenges - Showcase work to top researchers/engineers (with recruiters in the room) - Compete for $100k in model credit prizes Apply here: forms.gle/ijdHDbAJFZpFLi1b7 Competition overview: bit.ly/groundreasoningcup-st…

Agent memory is a simple and powerful way to do continual learning! With the new MemAlign method from Databricks Research, we can build better LLM judges from examples of human ratings, and they scale with more data. Now in Databricks and @MLflow. databricks.com/blog/memalign…

If you are excited about the intersection of reinforcement learning and highly complex economically valuable tasks --I can't think of a better place to spend the summer of 2026!