Releases

Databricks announced Unity AI Gateway as a unified governance and control plane across hosted and external AI assets.

Databricks expanded Agent Bricks with model choice, memory, document intelligence, sandboxes, registry support and multi-agent development capabilities.

Databricks announced Lakewatch, an agentic SIEM for detection, investigation and response on a security lakehouse.

Databricks introduced Genie Code, an autonomous AI partner for data work, pipeline maintenance and model operations.

Databricks redesigned AI Search with decoupled storage and compute to support billions of vectors at lower cost.

Agent Bricks Knowledge Assistant became generally available, powered by instructed retrieval and MLflow evaluation.

Databricks introduced ai_parse_document for multimodal PDF and document understanding integrated with Agent Bricks, AI Search and AI/BI.

Databricks introduced managed Model Context Protocol servers for Unity Catalog functions, Genie spaces and AI Search indexes.

Databricks introduced a simplified business-user front door to Genie spaces, dashboards and apps.

AI/BI Genie became generally available across AWS, Azure and Google Cloud, alongside a new knowledge store and planned Deep Research mode.

Databricks released MLflow 3 with unified tracking, evaluation, observability and governance for models and agents.

Databricks introduced Agent Bricks, which generates task-specific data and benchmarks to optimize agents for enterprise use cases.

Databricks introduced Lakebase, a serverless Postgres category designed for operational data and AI agents.

Databricks enhanced agent evaluation with customizable LLM judges, human review workflows and OpenTelemetry-based MLflow tracing.

Mosaic AI Gateway added support for OpenAI-compatible external and custom model providers with centralized permissions, routing and monitoring.

Databricks introduced code-first Custom Agents for building, deploying and evaluating production agent systems using any LLM.

Mosaic AI Agent Framework and Agent Evaluation entered public preview with integrated deployment, evaluation, tracing and governance.

Databricks introduced Apps for building and deploying secure data and AI applications directly on the platform.

Databricks introduced AI/BI, pairing AI-assisted dashboards with Genie conversational analytics built on a compound AI system.

Databricks introduced MLflow Tracing for end-to-end observability and debugging of generative-AI applications and agents.

Databricks launched a framework for authoring, evaluating, deploying and monitoring production-quality compound AI systems.

Databricks introduced a data intelligence engine that learns enterprise semantics and powers context-aware assistance and optimization.

MLflow AI Gateway provided a unified interface for routing requests to multiple commercial and open model providers.

Databricks introduced automated and human evaluation tooling for assessing generative-AI quality on enterprise data.

Databricks announced managed vector search integrated with the lakehouse for retrieval-augmented generation and semantic search.