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…

New research from Databricks: the context window is the only persistent substrate today's LLM agents have, and it floods fast. A single SQL query can return millions of rows that ride along in every subsequent turn, even when only one cell ever mattered. We hit this constraint every day in the agents we run in production, from Genie to Agent Bricks' Supervisor Agent to KARL. In a new post from the Databricks research team, we introduce MemEx: a programmable Python scratchpad that lets agents transform, slice, and persist tool outputs as typed objects in a live kernel. Same observe-act loop. Different action space. Across nine frontier and open-weight models on two enterprise agentic tasks (OfficeQA Pro and Enterprise Structured Retrieval): • Frontier models (Opus 4.6, Sonnet 4.6, Gemini 3.1 Pro) gain 2 to 5 accuracy points at 25 to 30% lower cost • Qwen 122B and Qwen 397B nearly double accuracy at 40 to 50% lower cost • Four of the five points on the OfficeQA Pro cost-accuracy Pareto frontier are MemEx configurations MemEx extends the code-as-action line (CodeAct, Anthropic Programmatic Tool Calling, Cloudflare Code Mode) with persistent scope across turns, eager spawn_agent for parallel sub-agents that share the parent's namespace, typed submit() for validated returns, and live-object scope injection. Built on aroll, the same Databricks agentic rollouts framework already powering those production systems. MemEx is rolling out across Databricks first-party agents and Agent Bricks soon. If you build on Databricks agents today, you'll be able to try it. Full write-up: databricks.com/blog/memex-pr…