Introducing Genie ZeroOps: Put your data and AI operations on autopilot

Introducing Genie ZeroOps: Put your data and AI operations on autopilot | Databricks Blog

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Data teams spend most of their time on maintenance, not building, and that burden is growing as AI makes it faster to ship pipelines and models.

Coding agents help build, but they can't automate operations since they are not part of the data platform and can't access metrics, logs and lineage. Importantly, they cannot safely access production data.

Genie ZeroOps is a background agent built into Databricks that autonomously monitors, investigates, and proposes fixes for data and AI assets such as pipelines, jobs, tables, ML models and more.

Data and AI work has always had a maintenance problem. Data pipelines break all the time due to not only code issues but also data problems such as upstream schema changes or late-arriving data. ML models drift, and degrading models keep serving confident, wrong answers long before anything throws an error. The burden of keeping data and AI assets running in production is falling on data teams, and it's only growing. The rise of LLMs and agentic tools has made it faster than ever to build pipelines and ship models. As a result, data teams report spending most of their time fighting fires rather than building.

Agentic operations with Genie ZeroOps

To help data teams with this operational burden, we’ve built Genie ZeroOps: an autonomous background agent that monitors your data and AI assets (such as pipelines, jobs, tables and ML models) and takes action before or when things go wrong. Because it runs inside Databricks, it has secure and easy access to:

Full observability: metrics, events, logs, and run history from the platform's observability layer.

Data lineage through Unity Catalog: the complete dependency graph of every asset, so it can trace failures to their true root cause.

Sandbox environments: Genie ZeroOps shallow clones production data (creating a table clone using metadata without duplicating the underlying data) into an isolated environment, applies permission guardrails and network isolation, and validates a proposed fix against real data without touching production.

Here's the process it runs for every failure:

Detect: Continuous monitoring with access to platform observability, including silent failures that show up in data quality metrics before they throw any errors.

Assess: Unity Catalog lineage gives Genie ZeroOps the full dependency graph. It can trace a failure to a code bug, a schema change three tables upstream, or bad data introduced by another pipeline.

Remediate: Agentic code generation produces the fix, with your development workflow (GitHub PRs, Jira tickets) as context.

Verify: Genie ZeroOps runs a secure sandbox with zero-copy clones of your data, scoped permissions, and network isolation. The proposed fix runs against real data there, never against production, and nothing is applied until you approve it.

Genie ZeroOps inbox UI showing incidents ordered by severity

Genie ZeroOps shows you a visualization of imapcted assets and the root cause analysis it performed using lineage data

Suggested fixes are provided with an indication of sandbox validation

Why coding agents can't solve data and AI operations

Why do you need a purpose-built agent for data and AI operations? Can’t you use the same coding agent that helps you build software and get the same results? The answer is – “no, not really”.

Coding agents were built for software engineering, but data engineering and AI are fundamentally different:

The context includes data, not just code. Pipeline failures are often caused by schema changes upstream, bad data propagating through a dependency chain, or silent corruption. None of which code alone can tell you about.

Failures can be silent and permanent. A data bug can sit quietly in a production table for weeks, poisoning downstream consumers. By the time you find it, the business implications have materialized.

Production data is sensitive and governed. Unlike code, it can't be freely copied, shared, or handed to an outside tool.

When something breaks, you need to: detect it, assess root cause, remediate with a fix, and verify it works without side effects.

Examine each step, and you’ll find coding agents typically fall short. For detection, they can lack context, such as telemetry or choke on extremely large context, like Apache Spark™ logs. For assessment, finding the root cause and its impact, they often lack access to lineage data. They also don’t have a purpose-built harness for data and AI work, which makes the process more costly and time-consuming. Coding agents can write code for remediation, but they often lack the context to do it right and can’t fix issues that are data-related. But the step that is most challenging for coding agents is verification.

Verification requires testing code fixes against real production data in an isolated environment. You can't give an external agent access to production data, and even if you did, running code against it risks side effects that can have devastating consequences.

For an agent to safely handle the verify step, it needs to be part of the data platform itself. Genie ZeroOps is part of the Databricks Platform, and that’s what makes it succeed where coding agents fail.

Machine learning workloads in particular showcase the benefits of a purpose-built agent for operations work.

Genie ZeroOps for machine learning

Production ML introduces some additional challenges to data engineering. A model can have no pipeline errors and still be producing bad predictions, which means keeping pipelines running isn't enough, you need to watch whether the model's outputs are still trustworthy.

When they aren't, Genie ZeroOps diagnoses the cause, builds a corrected candidate, and validates it before it touches live traffic. For a pipeline fix, it validates against a shallow clone of a table. For a model, it trains a candidate on corrected features and evaluates it against the same eval suite and criteria the production model was held to -- not a generic benchmark. It surfaces the candidate only if it's measurably better, and lets you ramp it on live traffic before it takes over.

What makes those fixes trustworthy is context. Genie ZeroOps for ML is built on the same foundation as Genie Code, Genie Ontology and native integration with the Databricks ML stack (Feature Store, MLflow, model serving, notebooks). It knows which features your model uses, how your team evaluates it, and what 'good' means for your business, so it reasons the way your senior ML engineers would.

You stay in control

You configure which assets Genie ZeroOps monitors and what it's authorized to do. Everything runs under Unity Catalog governance, so it can only access data your own credentials allow. Issues surface in an inbox-style UI, prioritized by severity, each with a root cause analysis and a proposed fix. Nothing gets applied to production without your approval.

The sandbox is the technical trust layer. Shallow cloning means the fix is tested with real data but production is never touched. Scoped permissions and network isolation mean the sandboxed environment can't reach outside its boundaries. What was tested is exactly what gets applied.

This is the value of Genie ZeroOps - it lets you scale your operations safely. It does the heavy lifting while you stay in control.

Genie ZeroOps is coming soon

Genie ZeroOps is entering private preview in the coming weeks, starting with support for jobs, pipelines, tables and ML workloads. Apps, and Lakebase databases are on the roadmap.

Talk to your Databricks account team to request early access. In the meantime, explore other members of the Genie family like Genie One and Genie Code.

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