Agent Bricks: Data + AI Summit 2026

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Last year at the Data + AI Summit, we launched Agent Bricks, ushering in a new way to build high quality agents that can reason over your data. Since launch, over 100k+ agents have been built, and we are now processing 1+ quadrillion tokens per year of agents. Customers such as AstraZeneca, 7-Eleven, Fox Corporation, and Block shipped agents built on Agent Bricks. This year at DAIS 2026, we are excited to announce the expansion of Agent Bricks as a comprehensive agent platform for developers. The missing 99% The rise of agentic coding, coupled by more powerful frontier models, have unleashed a Cambrian explosion of agents. Building agents with the many agent frameworks or harnesses in the ecosystem has never been easier. However, over the last year, we’ve learned that the core agent loop is just 1% of the work. The other 99% is the  hidden technical debt of agentic systems: token capacity, deployment, security, evaluation, monitoring, context, sharing (see below Figure). Agent platform Therefore, we observed developers stuck building infrastructure, not agents. This moment calls for an agent platform for developers. We believe an agent platform requires solving three critical challenges: Choice . Agents are increasingly composed of many subagents, and need model diversity to strike the right balance of quality and latency. Each model family has unique behaviors and are constantly out-performing each other with every release. Developers need broad model choice, all the way from frontier proprietary and open-source models to cheap but fast smaller models to models customized on their unique enterprise data. Context.   LLMs are powerful reasoning machines, but need the ability to retrieve and process the right context in order to make business-correct decisions. This is an extremely difficult problem, as the data estate is littered with missing or misleading information, or the needed context only resides in individuals or needs to be pieced together from multiple sources. Control . Agents are some of the most privileged actors in an enterprise, with access to sensitive data. The news is replete with agents accidentally deleting codebases, or prompt-injecting to leaking valuable information. And costs are exploding with employees ‘tokenmaxing’ their agentic coding leaderboards. Developers need ways to safely deploy agents, and to control costs so the business can afford to deploy agents at scale.

Building an agent platform that addresses these challenges requires connecting data with AI. After all, agents not only consume data via tools and context, but now also produce lots of data in its output, actions, reasoning traces, and memory – all of which must be governed and analyzed. This unification of data and AI is a feat uniquely positioned for Databricks. Agent Bricks We are beyond excited to announce the next evolution of Agent Bricks as our developer agent platform. What began as an experiment in agent building has expanded into a comprehensive platform for developers to build agents with any model and any harness, access data anywhere, and confidently deploy and control. We have all the building blocks, from secure sandboxes to agent memory to token capacity for developers:Databricks handles the infrastructure while you build impactful agents. Choice Models Agent Bricks offers all the frontier proprietary and open-source models in a single platform, natively integrated into our security boundary. Easily flex and test between different LLMs to balance agent behavior with latency and cost. In addition to OpenAI, Anthropic, Gemini, Qwen, we’ve just added support for Kimi. We’re thrilled to also announce a partnership with SpaceX to make the Grok models natively available on Databricks. "Databricks gives us a secure, governed foundation to run multiple models and switch providers as our needs evolve. All while keeping costs in check." — Gregory Rokita, VP of Technology, Edmunds For the last three years, we’ve been pioneering custom models: customers building models specialized on their enterprise data through  prompt optimization ,  fine-tuning, or  reinforcement learning . Our research team regularly trains custom models ranging from small models for subagent tasks to applying RL to large models as the core agentic model. Recently, we used reinforcement learning to train a custom data agent that is competitive with frontier models such as Opus and Sonnet in Genie-related tasks, while being significantly lower cost per query (see below Figure). Now, customers such as  Merck or  First American are using  AI Runtime to train LLMs specialized on their unique data.

Figure: Performance on an internal Genie benchmark, showing our Databricks Custom Model (red) is both higher quality and also lower cost than Opus and Sonnet models. Here, lower cost is to the right on the axis.

Agent harnesses We support any agent harness developers may want to use, from open-source frameworks such as LangGraph, Agno, CrewAI to harnesses such as Claude Code SDK or OpenAI Agent SDKs. Deploy these agents with horizontal autoscaling to Databricks Apps. We also offer a managed version of our open-source meta-harness  Omnigent , which we released last weekend, to orchestrate different harnesses.

Deploy custom agents with Databricks Apps

Context Retrieving the right data is no longer the RAG applications of yesteryear. Agents now have sophisticated tools to search, retrieve, and manipulate data during reasoning to identify the relevant context. Yet, the demands of today’s agent capabilities require traversing a complex and messy data landscape of outdated tables, unorganized Google Drive folders, confusing web search pages, and misleading documents. Often, the requisite context is simply unrecorded, existing only in the mind of a few key individuals. The rise of AI slop further pollutes the data estate with difficult-to-verify “facts”. Our research team has been solving critical problems here such as  agentic search ,  memory scaling ,  programmable scratch pads ,  evaluation , and  grounded reasoning . As part of Agent Bricks, these innovations are delivered in a few key components: Connect agents to data everywhere

By adding MCP support to Unity Catalog, agents in Agent Bricks can securely connect to external data sources such as Google Drive, JIRA, Slack, Github, and more. Our specialized search agents are able to leverage both...