MLflow

Job definitions now support trigger configuration and MLflow artifact location for AI runtime tasks. Pipeline connector options and GCP endpoint settings have been expanded with new configuration fields.

The SDK now supports job triggers and MLflow artifact locations in the Jobs API, enabling more flexible job scheduling and artifact management. New connector configuration options are available for Pipelines, and GCP endpoint settings have been expanded.

MLflow 3.15.1

MLflow 3.15.1 fixes version parsing issues on Databricks Serverless and corrects env_pack behavior on ARM client images. Scorer versioning documentation has been clarified.

Evaluating and Improving Agent Skills with MLflow

How to build measurable, testable, and continuously improving AI agent capabilities.

MLflow 3.15.0 introduces an MCP Registry for registering and sharing Model Context Protocol servers, enhances the Assistant with multi-provider LLM support and per-session token usage tracking, and enables proxy-less artifact transfers via presigned URLs to reduce server load and timeouts on large files. Additional improvements include sharable Runs table views, multi-modal image attachments for LLM judges to evaluate vision tasks, and numerous bug fixes across tracing, evaluation, gateway, and UI components.

Workspace ID validation for unified-provider resources shifted from plan time to apply time, reducing unnecessary API calls and eliminating false positives for restricted credentials and dynamic workspace IDs. The release adds a databricks_recipients data source for Delta Sharing, trace_location support for MLflow experiment traces in Unity Catalog, and fixes for VIEW column comment updates and access control rule set drift detection.

Simplify AI agent orchestration with Lakebase Postgres

Learn how to turn two Lakebase Postgres tables into a durable, crash-resilient queue for long-running AI agent tasks without operating an external broker, cache, or scheduler. This fully Databr

How Dow Built a Carbon Footprint Ledger on Databricks to Accelerate Sustainability at Scale

CFL) on the

Building a soccer coaching app on Databricks

Coach's Corner is a Databricks App that processes 25 fps match tracking data into a sub-second 2D/3D tactical bench with replays, event analytics, a scout chat, and an opponent-dossier agent. The end-to-end solution is powered entirely on the Databricks platform, utilizing Lakeflow pipelines for data refinement, DBSQL and Lakebase for rapid querying, and Unity Catalog-governed AI tools like Genie, Vector Search, and MLflow tracing.

Data-Native AI Agents: Why Agents Must Move to Your Data

AI agents must run directly within your data stack rather than in a separate, external stack to avoid compounding penalties like fragmented governance, high egress costs, and latency. By deploying data-native agents on the Databricks Data Intelligence Platform, enterprises can leverage an integrated stack of Unity Catalog, AI Search, MLflow, Lakebase, and AI Gateway to ship secure, trusted AI features faster.

The Databricks Go SDK updated job run structures to include deployment and version ID fields. MLflow experiment objects now support trace locations, and disaster recovery stable URLs include a stable workspace ID field.

Review Queues: The Human Step Towards Better AI

MLflow Review Queues turn AI trace review into a ticketing system featuring assignments, status tracking, and human evaluations. This new capability allows teams to stop tracking AI degradations and misbehavior

Multi-Harness AI Agents Need Multi-Layer Observability: Omnigent in MLflow

Omnigent unifies multi-harness agent orchestration and now delivers automatic observability across every agent with MLflow Tracing, requiring no code changes. This post details how Omnigent in MLflow provides multi-layer observability for multi-harness AI agents.

MLflow 3.14.0 adds one-command agent setup with Databricks support and durable low-latency Claude Code tracing, Review Queues for trace annotation and feedback collection, and @mlflow.test pytest markers for regression testing. Default model serialization formats change for sklearn to skops, PyTorch to pt2, and LightGBM to skops.

How to Manage your LLM Teams using MLflow's Role-Based Access Control

MLflow's new Role-Based Access Control (RBAC) helps LLM teams define reusable roles, isolate workspaces, and enforce fine-grained permissions across prompts, experiments, and AI Gateway resources. Learn how to manage your LLM teams using these new MLflow RBAC capabilities.

How Ecolab rebuilt retail intelligence on Databricks and Anthropic Claude

Ecolab rebuilt retail intelligence on Databricks and Anthropic Claude, converting 700-page FDA manuals into real-time answers for frontline staff using Foundation Model APIs and cutting compliance report compilation from two weeks to under two minutes. The solution, a native Databricks App with Lakebase Postgres and Unity Catalog, unifies nine siloed data sources and employs a multi-agent orchestration framework with Judge LLMs and MLflow tracing for personalized, continuously refined intelligence.

MLflow 3.13.0 introduces Role-Based Access Control with Admin UI, automatic trace archival to S3, and one-click observability for Claude Code and other coding agents. Breaking changes include a redesigned permission system (legacy APIs removed), MLServer removal from pyfunc serving, and requirement for MLFLOW_ALLOW_FILE_STORE=true flag for local file-based stores.

Route Claude Code Through MLflow AI Gateway

MLflow AI Gateway now supports routing Claude Code, providing full observability, budget controls, and guardrails for all your coding agent sessions. This integration requires no changes to your existing Claude Code usage.

MLflow 3.13.0rc0 completely overhauls Role-Based Access Control with unified permission APIs and a new Admin UI, and integrates Claude Code, OpenAI, Ollama, and OpenClaw as native assistant providers in the AI Gateway. The release adds trace archival with seamless retrieval, GenAI agent stress-testing, Kubernetes Helm chart support, and database replica routing for horizontal scaling.

From "What Happened?" to "What Will Happen?"

Conversational BI now delivers predictive answers in seconds, not days, by fusing Genie for dynamic feature engineering with TabPFN for zero-training prediction, orchestrated by Agent Bricks. This self-assembling pipeline eliminates data science bottlenecks for business users, providing a governed experience backed by Unity Catalog and MLflow.

Using MemAlign to Improve Evaluation of Traditional Machine Learning in Genie Code

MemAlign, an open-source MLflow framework, significantly improved the evaluation of traditional machine learning in Genie Code by reducing LLM judge error by 74-89% on key dimensions. This alignment was achieved with ~50 labeled examples, demonstrating the importance of both semantic and episodic memory for closing the gap between LLM judges and human experts.

From Black Box to Observability: Tracing OpenClaw with MLflow

MLflow Tracing now provides full observability for OpenClaw agents, moving them from black box to transparent. Learn how to quickly set up tracing to understand why your agent makes specific decisions, rather than just seeing the output.

MLflow 3.12.0 adds multimodal tracing with artifact attachments supporting rich rendering, extends coding agent tracing to Codex/Gemini/Qwen platforms, and introduces gateway guardrails for input/output safety. Trace table pagination improves performance, third-party scorer registration enables custom evaluations, and new provider support expands AI Gateway integration options.

MLflow 3.12.0rc0 adds automatic tracing for multiple AI coding assistants (Claude Code, Codex, Qwen Code, Gemini CLI, OpenClaw) through installable TypeScript plugins, and introduces guardrails for production agent safety in the AI Gateway. The release also supports multimodal trace attachments for images, audio, and files with UI visualization, plus a new mlflow.diffusers flavor for diffusion models with LoRA adapters.

AI observability for production: Seeing Inside Your Multi-Agent System with MLflow

MLflow now offers enhanced AI observability for multi-agent systems, providing crucial visibility into their internal workings. This helps practitioners prevent unintended actions like data purges or sensitive information leaks in production.

Structuring AI Evaluation and Observability with MLflow: From Development to Production

MLflow now offers enhanced tools for structuring AI evaluation and observability, including new APIs and UI features for logging LLM calls, prompts, responses, and metrics. This enables practitioners to systematically track, compare, and analyze model performance and behavior across development and production, facilitating iterative improvement and robust monitoring.

Enforce Content Policies at the Gateway with AI Gateway Guardrails

MLflow AI Gateway now supports configurable guardrails, using LLM judges to block or sanitize harmful content, PII, and custom policy violations. Enforce content policies at the gateway before requests reach your users or models.

TypeScript SDK 0.2.0 RC1

Release candidate for `@mlflow/vercel` TypeScript package with version 0.2.0: https://github.com/mlflow/mlflow/pull/22105

How to Prevent Runaway Agent Costs with MLflow AI Gateway

MLflow AI Gateway now helps prevent runaway agent costs by providing visibility into which part of your agent is driving up costs. This allows you to identify and address cost drivers before investing in the wrong optimizations.

MLflow 3.11.1 introduces AI-powered issue detection in traces, AI Gateway budget alerts and spending controls, trace graph visualization, native Databricks gateway provider, and pickle-free model serialization. TypeScript SDK packages are now @mlflow-scoped and LiteLLM is no longer required for GenAI evaluation.

Harness Your OpenHands Agent with AI Observability and Governance

MLflow now supports tracing, evaluating, and governing OpenHands agents, capturing every step of their autonomous operations. This enables practitioners to monitor agent actions, assess output quality, and manage LLM costs effectively.

Testing and Refining Claude Code Skills with MLflow

MLflow tracing and LLM judges can now test Claude Code skills. This enables a self-improvement loop where Claude Code refines its own abilities.

Tracking and Debugging AI Safety Evaluations with Inspect AI and MLflow

Inspect AI evaluations now integrate with MLflow for experiment tracking and execution tracing via the inspect-mlflow package. This enables practitioners to track and debug AI safety evaluations using familiar MLflow tools.

MLflow Workspaces: Shared Deployment Without Separate Servers

MLflow Workspaces are now available, enabling shared MLflow deployments across multiple teams by adding a logical organization and permission layer. This allows teams to scope experiments, models, traces, prompts, AI Gateway resources, and artifacts within their own workspace.

Your Agents Need an AI Platform

MLflow 2.12 ships with new features for building and managing AI agents, including enhanced logging for agent traces, evaluation tools, and versioning capabilities. Leverage MLflow as your unified platform for developing, deploying, and governing reliable AI agents in production.

Control LLM Spend with AI Gateway Budget Alerts and Limits

AI Gateway now supports budget policies to control LLM spend with alerts and request limits. Set spending thresholds, receive webhook alerts, and automatically reject requests when budgets are exceeded.

This release introduces AI-powered issue identification for agent traces, budget alerts and limits for AI Gateway spending, and an interactive graph view for trace hierarchies. It also includes native OpenTelemetry GenAI convention support, Opencode tracing integration, UV package manager support, and pickle-free model serialization options for enhanced security.