Discover new BigQuery Data Transfer Service capabilities. Automate low-cost data ingestion from diverse sources directly into your data warehouse.

Traditional integration platforms were built for a world of predictable, human-configured workflows. But with enterprise software rapidly incorporating agentic AI capabilities, that world is changing fast. Agentic iPaaS represents a fundamental architectural shift where intelligent agents reason, adapt, and execute integrations autonomously, moving beyond simple "if-then" automation to goal-oriented systems that make real-time decisions.

Your data pipeline breaks at 2 AM. Again. By morning, corrupted data has cascaded through dashboards, reports sit empty, and your team spends half the day tracking down root causes instead of building features. This scenario plays out across organizations daily. Data engineers spend 44% of their time firefighting pipeline failures rather than delivering value.

A single source of truth doesn’t just come from centralizing data. You have to engineer context from it, too.

Two engineers used AI coding agents to design, build, and ship a production-ready status page — here's what worked, what didn't, and what we'd do differently.

Learn about the latest innovations to help improve BigQuery price-performance with autonomous query processing.

Software workloads that process large volumes of real-time data are becoming common. Decades working in this domain has taught me that building and operating reliable and maintainable real-time data products requires permissive access to the context of the environment. This article explains how to approach agentic engineering and apply it when building real-time data products inside an agentic software factory.