Databricks Launches Genie Code: Bringing Agentic Engineering to Data Work
Genie Code turns data engineering, data science and analytics ideas into autonomous production systems
Databricks, the Data and AI company, launched Genie Code, an autonomous AI agent that fundamentally changes how data work gets done. Genie Code can carry out complex tasks such as building pipelines, debugging failures, shipping dashboards, and maintaining production systems. On real-world data science tasks, Databricks found Genie Code more than doubled the success rate of leading coding agents. Just as agentic coding tools have transformed software engineering, moving developers from autocomplete-style assistance to agent-driven development, Genie Code brings the same paradigm shift to data engineering, data science, and analytics.
Genie Code is a new addition to Genie, which lets any knowledge worker chat with their data and get trusted answers instantly using the context and semantics captured by Unity Catalog. Genie Code extends this approach to data professionals, handling the complex engineering required to go from idea to production across all enterprise data. Additionally, today Databricks announced the acquisition of Quotient AI, an innovator in evaluation and reinforcement learning for AI agents, to embed continuous evaluation directly into Genie and Genie Code.
Rise of Agentic Data Work
Today’s data tools treat AI as a helper — writing code, running local tests, iterating on it. This leaves data teams doing the hard work of planning, orchestrating, operating, validating and maintaining. Genie Code inverts this approach. It reasons through problems, plans multi-step approaches, writes and validates production-grade code, and maintains the result — all while keeping humans in control of the decisions that matter.
“Software development has shifted from code-assistance to full agentic engineering in the past six months,” said Ali Ghodsi, Co-founder and CEO of Databricks. “Genie Code brings this revolution to data teams. We’re moving from a world where data professionals are assisted by AI to one where AI agents do the work, guided by humans. We are calling this Agentic Data Work. It will fundamentally change how enterprises make decisions.”
What Genie Code Does
Existing agentic coding tools have trouble accomplishing data tasks because they lack access to critical context like lineage, usage patterns and business semantics. Genie Code helps teams bridge the context gap to ensure the high levels of accuracy and governance required for production environments. Genie Code:
- Acts as an expert machine learning engineer: Genie Code handles full ML workflows end-to-end. It reasons through complex problems to plan, write, and deploy models, while logging experiments to MLflow and fine-tuning serving endpoints for peak performance.
- Embeds deep data engineering expertise: While a novice engineer might write a script that works on test data, Genie Code designs like a senior architect. It accounts for the differences between staging versus production environments, builds workflows for change data capture and applies data quality expectations.
- Proactively maintains and optimizes: Genie Code monitors Lakeflow pipelines and AI models in the background to triage failures and investigate anomalies. It autonomously analyzes agent traces to fix hallucinations and tunes resource allocation before a human intervenes.
- Understands enterprise context: Integrated with Unity Catalog, Genie Code enforces existing governance policies and access controls. It understands business semantics and audit requirements and federates enterprise data, including data from external platforms.
- Improves over time: Genie Code grows smarter the more teams use it. Through persistent memory, it automatically updates internal instructions based on past interactions and coding preferences. On real-world data science tasks, Databricks found Genie Code more than doubled the success rate of leading coding agents (from 32.1% to 77.1%).
“At SiriusXM, Genie Code supports everything from authoring notebooks and complex SQL to reasoning through table relationships and debugging pipelines,” said Bernie Graham, VP of Data Engineering, SiriusXM. “It acts as a hands-on development partner that helps our data teams deliver high-quality work in less time.”
Read More: Why Manufacturing Leaders Are Turning to Domain-Specific Language Models for Operational Excellence
“Genie Code changes how our data teams operate,” said Emilio Martín Gallardo, Principal Data Scientist, Data Management & Analytics at Repsol. “Instead of stitching together notebooks, pipelines, and models manually, we can hand off complex workflows to an AI partner that understands our data, governance, business context, and internal libraries such as Repsol Artificial Intelligence Products. It accelerates everything from time series forecasting to production deployment, without sacrificing rigor or control.”
Write to us [wasim.a@demandmediaagency.com] to learn more about our exclusive editorial packages and programmes.
