Databricks aims to optimize agent building for enterprises with Agent Bricks

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.