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Databricks Agent Bricks review: the best enterprise agent platform for data and ML teams?

Answer in brief: Databricks Agent Bricks ranks #8 on our shortlist. It is the best-for pick for data and ML teams whose governed enterprise data already sits in Databricks, and ISG ranked Databricks second overall in its 2026 AI Agents Buyers Guide. Agent Bricks is a set of interoperable building blocks rather than a single framework: durable serving, memory, MCP tools, MLflow evaluation, and Unity Catalog plus Unity Gateway governance. It works with any framework or model. It is built for developers, and there is no single price.

Top pick for lakehouse-centric data teams. Agents that sit next to your governed data, with evaluation and spend controls built in. Visit Databricks Agent Bricks →

This is independent research from Databricks documentation and product pages, and from ISG's public summary. We have not tested the product hands-on.

From prototype to governed production on your own data

Agent Bricks layers managed services around your agent code:

Model access through Unity Gateway and Foundation Model APIs.
Compute through DurableAgentServer, Agent Runtime on Databricks Apps, and Sandbox for running agent-written code safely.
Context through memory, sessions, MCP tools, AI Search and Genie.
Quality through MLflow.
Governance through Unity Catalog and Unity Gateway.

DurableAgentServer supports synchronous, streaming and background requests, with persistent run state and crash recovery. A CLI scaffolds, runs and deploys agents.

Who it fits best

Data, analytics and ML teams already invested in Databricks.
Companies whose most valuable agent context is governed tables, documents and features.
Teams that want rigorous evaluation before agents reach users.

Why buyers shortlist it

Framework and model freedom. Databricks says any framework works. Documented examples include LangGraph, the OpenAI Agents SDK, LangChain and LlamaIndex, with models from the GPT, Claude, Gemini, Llama and DeepSeek families.
Serious evaluation. MLflow tracing, built-in LLM judges, custom scorers, evaluation datasets, expert feedback and monitoring of sampled production traffic.
Governance you can enforce. Unity Catalog governs models, tools, MCP services and agents. Unity Gateway applies authorization, rate limits, budgets, usage tracking and guardrails.
Analyst recognition. ISG ranked Databricks second overall in 2026.

Honest drawbacks

Developer-first. Not a builder for business users.
No single price. You price a mix of underlying services.
Some parts are Beta. Request and response service policies are labeled Beta. The CLI's built-in templates cover only LangGraph and the OpenAI Agents SDK.

Pricing model

Usage-based, at the existing rates of the underlying Databricks products consumed, tracked per second. Databricks' general pricing page shows AI workloads starting at $0.07 per DBU. That is a category starting price, not an all-in Agent Bricks price. Actual cost depends on models, serving, evaluation, search, storage, cloud and region.

Best alternatives

Amazon Bedrock AgentCore: modular agent infrastructure on AWS.
Gemini Enterprise Agent Platform: Google's developer platform.
Microsoft Foundry Agent Service: hosted agents on Azure with Entra identities.

Agent Bricks FAQ

How is Databricks Agent Bricks priced?

Usage-based, at the rates of the underlying Databricks services you consume. There is no separate platform license.

Which frameworks work with Agent Bricks?

Any framework, according to Databricks, including LangGraph, the OpenAI Agents SDK, LangChain and LlamaIndex.

How does Agent Bricks evaluate agents?

Through MLflow: tracing, built-in LLM judges, custom scorers, evaluation datasets, expert feedback and production monitoring.

Sources

Visit Databricks Agent Bricks →

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Last updated: 2026-11-09.