Most AI coding agents are built for software engineers. They read files, write functions, and push code. Data teams need something different: context from table lineage, usage patterns, schema history, and business semantics, none of which lives in a code file. That gap is why general-purpose coding agents struggle with production data workflows even when they perform well on standard
Enterprises have spent the past two years running AI pilots. Most produced results in sandboxes that never reached production. The gap was rarely the model. It was fragmented data, undefined governance, and infrastructure never built for agents at scale. At Databricks Data + AI Summit 2026, held in San Francisco, Databricks addressed all three with a wave of releases, from
For years, sharing data across organizational boundaries followed a predictable pattern. You copied it. That meant stale data, duplicated storage costs, compliance headaches, and a manual update loop that never quite kept pace with how fast the source changed. Delta Sharing, launched by Databricks in 2021, was the first serious attempt to break that pattern with an open, zero-copy protocol.
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