Databricks aims to optimize agent building for enterprises with Agent Bricks

Databricks has released a beta version of a new agent building interface to help enterprises automate and optimize the agent building process.

Agent Bricks is a new generative AI-driven interface combining technologies developed by MosaicML, which Databricks acquired in 2023, includng TAO, synthetic data generation API, and the Mosaic Agent platform.

However, it is not a rebranded Mosaic Agent Platform, said Robert Kramer, principal analyst at Moor Insights and Strategy, but rather a higher-level, automated abstraction that streamlines agent development for enterprises.

Agent Bricks is intended for use mainly by data professionals and developers, and can be accessed via the “Agent” button in the lakehouse software provider’s Data Intelligence platform.

Using Agent Bricks

Users begin by selecting from a variety of task types they want to execute via agents, such as extracting information from documents, creating a conversational agent, or building a multi-agent workflow system.

Next, they provide a high-level description of the type of agent or agents they are trying to build and attach different knowledge sources for the required task.

Agent Bricks then automatically creates a custom evaluation benchmark for the specified task, said Databricks’ CTO of neural networks, Hanlin Tang, said, giving the example of an agent for customer support.

“We will build a custom LLM judge that’ll measure whether the customer will churn during that support call, or the customer is happy or unhappy. Once we have the benchmark for the task, Agent Bricks then looks at how to optimize the agent itself,” Tang added.

During the optimization phase, Agent Bricks uses techniques including synthetic data generation, prompt engineering, model selection, and fine tuning to deliver an agent that is optimized for quality and cost that enterprises can deploy.

Optimization

Databricks is pitching Agent Bricks as a way to take the complexity out of the process of building agents, as most enterprises don’t have either the time or the talent to go through an iterative process of building and matching an agent to a use case.

“Agent Bricks is notable for automating the agent creation lifecycle from evaluation to optimization, an area where most competitors focus on hosting or orchestrating models rather than lifecycle automation,” said Kramer.

“Snowflake Cortex, for example, supports hosted AI agents but lacks synthetic data and auto-optimization features. AWS Bedrock and Azure OpenAI provide model hosting and tooling but do not offer integrated evaluation and cost tuning at this level,” he said.

Teradata doesn’t offer direct agent-building capabilities either, while Salesforce’s Agentforce platform, though offering evaluation tools for agents, doesn’t automate the process and leaves a lot of engineering to data professionals and developers.

At Constellation Research, Principal Analyst Michael Ni pointed out that though Agent Bricks automates and optimizes the agent building process, it will not replace data engineers, but rather help them save them time in building agentic applications.

“Data engineers still oversee integration and deployment, but they’re no longer blocked by repetitive, low-value scaffolding work,” Ni said, adding he doesn’t see Agent Bricks as a point-and-click tool for non-technical users.

Support for MCP

As part of the new interface, Databricks is adding some templatized agents that enterprises can use out-of-the-box: Information Extraction Agents for getting structured data from unstructured data, Knowledge Assistant Agent for supporting conversational agents, Multi-Agent Supervisor for building multi-agent systems, and Custom LLM Agent for custom tasks.

These agents are use-case templates or archetypes designed for common enterprise scenarios and act as starting points that can be customized based on organizational data and performance requirements, rather than rigid building blocks or mandatory frameworks.

Agent Bricks also supports Anthropic’s Model Context Protocol (MCP), an increasingly popular framework for connecting agents across disparate technology stacks or systems.

The Multi-Agent supervisor inside Agent Bricks can read and accept an MCP server as an agent, making agents on other systems accessible, said Databricks’ Tang, adding that an agent produced on Agent Bricks can also be exposed as an endpoint in order to connect to other agent systems.

Databricks is also working on support for Google’s A2A protocol, Tang said, cautioning that development is in “very early days.”

A different approach to agent lifecycle management

Agent Bricks takes a different approach to agent lifecycle management when compared to similar offerings from other vendors.

While most vendors have built agent lifecycle management tools inside their agent builders, Databricks is leveraging Unity Catalog and MLflow 3.0 for managing agents built on Agent Bricks — meaning ongoing AgentOps tasks, such as, monitoring, evaluation, deployment, and rollback, are handled by MLflow 3.0 and Unity Catalog.

Snowflake, on the other hand, integrates agent lifecycle management within Cortex, while AWS and Azure embed monitoring directly into their agent environments.

Kramer said that enterprises with smaller teams may think twice before adopting Agent Bricks as Databricks’ approach requires users to work across multiple services. “This separation may slow adoption for teams expecting a unified toolset, especially those new to Databricks’ platform,” he said.

But, said Bradley Shimmin, the lead of the data and analytics practice at The Futurum Group, some enterprises may align with Databricks’ approach towards agent lifecycle management: an agent, like a data table or machine learning model, is an asset and should be governed by the same central catalog.