Databricks Agent Bricks

Tracked · below panel floor — earned mentions are accruing toward the next scored window.

Measures earned, engagement-weighted share of voice across the GTM voices panel. Methodology →

Databricks positions Agent Bricks as the governed system of record for building AI agents on a company's own data, model-agnostic across OpenAI, Anthropic, Google and open-source options, with auto-generated evaluation data, LLM-as-judge scoring and Unity Catalog-based access controls aimed at reining in enterprise 'agent sprawl' rather than adding another point tool. Customer names surfacing around it, including AstraZeneca, Flo Health, Hawaiian Electric, Lippert and the North Dakota University System, sit inside Databricks' existing enterprise data-platform base. As a named product, Agent Bricks still has a quieter footprint in GTM conversation so far, early relative to the attention Databricks itself draws as a company.

Measures how easily your agents can build on it — API, MCP, CLI, SDK, docs depth. Methodology →

Agent Bricks ships production agent surfaces broadly: a REST API for deploying agents as serverless endpoints, native Model Context Protocol support with centralized credential management for tool integration, and SDK support for building and deploying from a developer's own environment, with MLflow layered in for run-level observability. No public CLI ships yet. The build-out reads as governance-first, with Unity Catalog access controls and lineage tracking sitting underneath the agent surfaces rather than beside them.