ADBC and the Future of Universal Data Connectivity for Agentic Systems

Agents are becoming first-class users of the data stack, but they inherit 1990s-era connectivity: slow row-oriented APIs, heavy SDKs, and brittle system-specific connectors. For AI-driven workflows making fast tool calls across systems, this isn’t just slow—it’s terrible agent ergonomics, leading to failures, retries, and need for human intervention.This talk explains why ADBC (Arrow Database Connectivity) is emerging as the universal connector for agentic data workflows: a vendor-neutral API and driver standard that ships Arrow columnar results, enabling fast data access across languages and engines. We’ll show why this matters for agents: frequent queries, tight latency/cost budgets, and the need for safe, reliable access to the tabular truth.You’ll leave with a clear model for where ADBC fits in lakehouse architectures, why it aligns with Databricks and open standards, and a how to move from framework-heavy integrations to lean, CLI-first building blocks guided by agent skills.