At the Data + AI Summit, Databricks announced the launch of Agent Bricks, a new offering that allows businesses to build and deploy AI agents using their own data, without the need for manual tuning or complex tooling.
Available in beta starting today, Agent Bricks is positioned as an automated system that transforms a high-level task description and enterprise data into a production-grade agent.
Ali Ghodsi, CEO and co-founder of Databricks, described it as “a whole new way of building and deploying AI agents that can reason on your data.” He added, “For the first time, businesses can go from idea to production-grade AI on their own data with speed and confidence, with control over quality and cost tradeoffs.”
Agent Bricks automates the entire process of AI agent development. It uses research developed by Mosaic AI to generate synthetic data tailored to a customer’s domain and builds task-specific benchmarks to evaluate agent performance.
The system then runs a series of optimisations, allowing users to choose the version that best balances accuracy and cost. The result is a deployable agent that operates with consistency and domain awareness.
The platform supports a range of use cases across industries. An Information Extraction Agent can convert unstructured content like PDFs and emails into structured fields such as names and prices. A Knowledge Assistant Agent provides accurate, data-grounded answers to user queries, reducing the kind of errors often seen in traditional chatbots.
The Multi-Agent Supervisor allows coordination between multiple agents and tools like MCP to manage workflows, including compliance checks and document retrieval.
Meanwhile, a Custom LLM Agent handles specific text transformation tasks, such as generating marketing content that aligns with an organisation’s brand voice.
Databricks said the product addresses a key issue in the AI agent space, which is that most experiments fail to reach production due to a lack of evaluation standards, inconsistent performance, and high costs.
According to the company, Agent Bricks resolves these challenges by offering domain-specific, repeatable, and objective evaluations, all within a workflow that requires no stitching together of multiple tools.
Early adopters are seeing results across sectors. AstraZeneca used Agent Bricks to extract structured data from over 400,000 clinical trial documents without writing any code. Joseph Roemer, head of data & AI at the company, said they had “a working agent in just under 60 minutes.”
At Flo Health, the tool helped improve the medical accuracy of AI systems while meeting internal standards for safety and privacy. “By leveraging Flo’s specialised health expertise and data, Agent Bricks uses synthetic data generation and custom evaluation techniques to deliver higher-quality results at a significantly lower cost,” said Roman Bugaev, the company’s CTO.
The announcement was accompanied by the release of two additional tools. Databricks now offers support for serverless GPUs, giving teams access to high-performance compute without the operational burden of managing GPU infrastructure. This enables users to fine-tune models and run AI workloads on demand.
Databricks also launched MLflow 3.0, a redesigned version of its AI lifecycle management platform. Tailored for generative AI, MLflow 3.0 includes prompt management, human feedback loops, LLM-based evaluation and integration with existing data lakehouses. The new version allows teams to monitor and debug AI agents across any platform and is downloaded over 30 million times a month.
According to Databricks, the combination of Agent Bricks, serverless GPU support, and MLflow 3.0 makes its platform the most complete environment for building, tuning and deploying enterprise-grade generative AI systems.
