Boomi, the data activation company, has rolled out new capabilities in the Boomi Enterprise Platform to help businesses turn raw data into real-time insights for artificial intelligence (AI), analytics, and automation.
The update focuses on “data activation,” a process that delivers the right data, with context, at the right time across systems. The platform now adds semantic context for AI agents, expands SAP data integration using change data capture (CDC), improves visibility into AI-driven workflows, and introduces a Europe-based platform instance for data residency compliance.
“What we are seeing now is clear: AI only delivers value when data is properly activated, trusted, and governed first,” said Steve Lucas, chair and CEO at Boomi. “Organizations don’t need more pilots, they need action-ready data. With these innovations, we’re providing the infrastructure to put data in motion, ensuring agents are grounded in context and every action is governed with precision.”
One of the additions is Boomi Meta Hub, which creates a shared source of truth across enterprise systems. Built on the company’s master data management and integration tools, it aligns data standards so AI agents and users work from consistent, reliable information instead of fragmented datasets.
For companies operating in regulated markets, Boomi introduced a dedicated European platform instance. The setup keeps customer data, metadata, and processing within Europe, helping organizations comply with General Data Protection Regulation (GDPR) requirements while improving system resilience and performance.
Boomi also strengthened SAP integration with a new SAP Data Connector that enables real-time data extraction without custom coding. Using multi-table ingestion and CDC, the tool helps businesses move fresh, governed data into cloud and lakehouse platforms, supporting AI models and analytics.
To address concerns around AI transparency, the platform adds governance tools for Snowflake Cortex Agents through the Boomi Agentstudio Agent Control Tower. Features such as Agent Session Logs and observability metrics provide visibility into how AI agents operate, including latency, errors, and token usage, across more than 30 providers.
The updates aim to help enterprises move faster from AI experimentation to production, with built-in recommendations that guide how agents are deployed and integrated at scale.