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.

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:

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:

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.