Data News

Databricks Unveils Genie Code, Bringing Agentic Engineering to Data Work

Ali Ghodsi, Co-founder and CEO of Databricks

Databricks, the Data and AI company, has launched Genie Code, an autonomous AI agent designed to transform how data engineering, data science, and analytics teams build and manage production systems. Positioned as a breakthrough in “Agentic Data Work,” Genie Code takes data professionals from AI‑assisted workflows to fully agent‑driven processes capable of planning, executing, validating, and maintaining complex data operations with minimal human intervention.

Built as an extension of the company’s Genie platform, which already enables knowledge workers to interact with enterprise data conversationally using Unity Catalog semantics, Genie Code pushes that capability deeper into technical domains. Databricks says the agent can autonomously construct data pipelines, debug failures, ship dashboards, and maintain production workloads end‑to‑end. In real‑world tests, Genie Code more than doubled the task‑completion success rate of leading AI coding agents.

“We’re moving from AI-assisted coding to AI agents doing the work guided by humans.”

— Ali Ghodsi, Co-founder and CEO of Databricks

Databricks also announced its acquisition of Quotient AI, a company specialising in evaluation and reinforcement learning for AI agents. Quotient’s automated monitoring and regression detection will now be integrated into Genie and Genie Code, enabling continuous improvement in production environments.

“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.”

Genie Code is engineered to act like an expert data professional:
• It plans and deploys machine learning workflows, logging experiments in MLflow and optimising model endpoints.
• It applies senior‑level data engineering judgment, handling staging‑vs‑production nuances and building resilient pipelines.
• It proactively monitors Lakeflow pipelines and AI models, resolving anomalies and preventing failures.
• And by leveraging Unity Catalog, it enforces enterprise governance policies and understands business semantics.

Early adopters like SiriusXM and Repsol report significant acceleration in data delivery, reduced manual overhead, and improved reliability.

Databricks says Genie Code will redefine how enterprises operationalise data shifting the role of AI from assistant to autonomous engineering partner.

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