By the time most enterprise data reaches the systems that are meant to act on it, it's often stale. That lag is the difference between AI agents making a useful answer or a costly mistake. Agentic AI can only deliver intelligent decisions when it has continuous access to fresh information.
The demand on data engineering teams has shifted toward real-time pipelines and event-driven architectures as more organizations push agentic AI into production — highlighting the need to connect and govern more sources even as they undergo constant change. But what teams are being asked to build has outpaced what their data platform can currently support.
At Summit 2026, Snowflake is strengthening the platform to help data engineering teams succeed in the AI era. This includes notable releases like a native Apache Kafka-compatible streaming service and AI-powered capabilities that reduce data movement and migration costs.
These improvements reduce the time data engineers spend on infrastructure management and manual orchestration, enabling them to spend less time on plumbing and more time on the outcomes that AI makes possible with Snowflake CoCo serving as the common thread that turns complex setup into a guided conversation.
