Manage, govern, and orchestrate longer-running data and ML development workflows on Databricks.
by Julia Powell, Gal Oshri and Weston Hutchins Genie Code helps data and ML teams build and improve systems faster on Databricks. Over the past year, Databricks’ Genie products have grown over 10x and are used by 90% of Databricks customers. Teams are using it to build models and pipelines, debug failures, create dashboards, analyze data in notebooks, and improve production systems.
At Data + AI Summit 2026, we’re expanding Genie Code for more complex, agentic data and ML work. We’re introducing a new full-page command center, upgrades for production data and ML engineering, and scheduled tasks.
These updates are part of a broader shift across Databricks toward AI-native data and ML workflows. Genie Code helps data teams build, debug, and improve data and ML systems, and we introduced Genie ZeroOps to extend agentic automation to operations. Together, these products help teams move faster across the full lifecycle, from building systems to operating and improving them over time.
Here’s what’s new:
Data and ML development rarely happens in a single prompt. A user may need to inspect existing logic, update multiple assets, run code, review outputs, and refine the next step based on results. That work can span notebooks, SQL, Lakeflow pipelines, dashboards, jobs, models, serving endpoints, and Unity Catalog assets.
We've redesigned the Genie Code experience to give teams a dedicated command center for this kind of complex data and ML work. Instead of managing longer tasks in a smaller side panel, users can use a full-page experience to describe a task, track progress, review outputs, and continue iterating.
Teams can manage multiple Genie Code threads, see when a thread is executing or waiting for input, and return to each one when new results are ready. They can rename threads, search previous conversations, and stay oriented as projects evolve.
Genie Code has completely changed how I work. I run upwards of 15 parallel threads scoped to different notebooks and assets every day, and managing all of that across tabs is one of the biggest sources of friction in my workflow. Full page Genie Code with concurrent sessions would give me a true workspace for running everything in parallel without constantly losing context.— Moritz Schiek, Solution Consultant, Bosch
The command center also makes customization easier to discover. Instructions, skills, and connectors are more visible, so teams can guide Genie Code with the right standards, tools, and workspace knowledge.
To access the full-page command center, open the Genie Code side panel and click the maximize button in the top left.
In an ML project, the model is the small part. Most of the time goes to the engineering around it: turning raw data into features, running experiments, getting a candidate to production, and keeping it healthy once real traffic arrives. That work is slow and expensive, and it is why most teams run far fewer models than they have use cases for.
Genie Code for machine learning goes after that engineering. It is a set of capability and intelligence upgrades built into Genie Code, so you do not adopt a new tool. The same agent you already use becomes a specialist for production ML engineering across your existing Databricks ML stack.
Genie Code's expertise comes from two places. The first is Databricks. We have run production ML with customers for more than a decade, and we have seen where models break, where teams lose time, and what separates a model that works from one that looks right and fails without warning. Genie Code applies those lessons as it works, handling the details a seasoned practitioner would, like correcting for class imbalance and checking feature quality.
The second is your team. A generic coding agent has not seen your past experiments, your business metrics, your evaluation sets, or how you weigh one objective against another, so it guesses. Genie Ontology closes that gap. It learns how your team builds features, trains models, and evaluates candidates, and Genie Code follows those patterns instead of falling back on irrelevant defaults.
With both kinds of knowledge, Genie Code is a stronger partner for day-to-day model development. It writes features in your team's patterns, makes coordinated edits across the many files involved, runs and debugs your code, and compares candidates with your own evaluation scripts. You stay in the loop and decide what to keep.
Genie Code is now natively integrated with the entire Databricks ML stack. The latest upgrades:
The result is an agent that finishes real-world data science tasks far more often than a generic coding agent.
With Genie Code, we moved from raw data to a governed, production-ready ML workflow in 90 minutes. Because it uniquely understands production ML workflows on Databricks, it helped us create Delta tables, explore the data, train and compare models, register them with MLflow and Unity Catalog, and deploy the champion model to a serving endpoint, with time left to optimize for the business outcome that mattered most.— Radu Dragusin, Principal Engineer, Data & AI, Danfoss
Until now, Genie Code has been primarily interactive: you ask, it responds, and you stay in the loop as the work progresses. Scheduled tasks change that.
Coming soon, scheduled tasks will let Genie Code do work on your behalf even when you are not at your laptop, then hand you the results to review when you return. A scheduled task starts with a prompt and, optionally, a relevant asset such as a notebook, workflow, or dashboard. When it runs, Genie Code creates a thread with the results, which users can review, refine, or continue interactively.
For example, a data team could ask Genie Code to check overnight job results, summarize pipeline runs, explain a change in a dashboard metric, prepare a weekly analysis, or review model performance before a team meeting. The user does not need to rerun the prompt manually or stay in an active chat while the work happens. Scheduled tasks move Genie Code from interactive assistance toward autonomous work. They help teams keep important workflows moving while results stay visible, reviewable, and grounded in Databricks context.
Genie ZeroOps extends this approach into production operations. It watches live systems, investigates issues, and prepares fixes for teams to review and approve. For ML systems, that can include model drift, serving errors, and upstream pipeline problems. For data engineering systems, it can help teams move from monitoring and diagnosis toward repair and optimization.
If you have a Databricks workspace, you already have Genie Code. Open it in your workspace to try the full-page experience today. To see how the Genie family extends into production operations, read the Genie ZeroOps launch blog. Subscribe to our blog and get the latest posts delivered to your inbox.