Starburst, the data platform for apps and AI, today announced at AI & Datanova, a new set of capabilities designed to operationalize the Agentic Workforce—a paradigm where humans and AI agents collaborate seamlessly across workflows to reason, decide, and act faster and with confidence. With new, built-in support for model-to-data architectures, multi-agent interoperability, and an open vector store on Iceberg, Starburst delivers the first lakehouse platform that empowers AI agents, with unified enterprise data, governed data products, and metadata, empowering humans and AI to reason, act, and decide faster while ensuring trust and control.
Unlike legacy platforms that require data movement or rely on black-box retrieval, Starburst gives AI agents secure, governed access to data wherever it resides, on-premises or in the cloud, at enterprise scale. This federated, model-to-data approach helps organizations maintain sovereignty, reduce costs, and avoid compliance pitfalls, especially in highly regulated industries or cross-border environments.
To further strengthen enterprise confidence in AI, Starburst is introducing advanced observability and visualization features for its agent framework. Organizations can now monitor usage of LLM interactions, set guardrails with usage limits, and view activity through intuitive dashboards. In addition, Starburst’s agent can visualize responses into charts and graphs giving teams not only accurate answers but also clear, actionable insights. These capabilities provide a new level of transparency, governance, and usability as enterprises scale AI adoption.
“With the Agentic Workforce, enterprises move beyond analyzing data to taking intelligent action,” said Justin Borgman, CEO and Co-Founder of Starburst. “Our latest innovations bring models directly to governed data, allow AI agents to interoperate across multi-agent ecosystems, and provide open access to vector stores without lock-in. Starburst empowers organizations to scale AI securely and confidently across clouds, borders, and business-critical use cases.”
“Enterprises have been looking for a way to bring structured data and governance into AI workflows, and the emergence of Model Context Protocol (MCP) has made that possible,” explained Andrew Brust, Founder & CEO, Blue Badge Insights. “But Starburst takes this much further. Not only does it unify vector store connectivity for RAG, but it provides AI agents with secure access to governed data products, leveraging Trino’s federated architecture to do it all without data movement, thus ensuring compliance. The result is a pragmatic path for enterprises to scale AI with the trust, transparency, and regulatory control their business environments demand.”
Key Innovations Driving the Next Generation of AI and Analytics
Starburst’s new AI capabilities are built upon the core principle of flexibility, giving organizations the freedom to choose between model-to-data and data-to-model architectures. This approach enables enterprises to scale AI securely, while preserving sovereignty, reducing infrastructure costs, and ensuring compliance. These enhancements include:
- Multi-Agent Ready Infrastructure: A new MCP server and agent API allows enterprises to create, manage, and orchestrate multiple AI agents along-side the Starburst agent. This enables customers to develop multi-agent and AI application solutions that are geared to complete tasks of growing complexity.
- Open & Interoperable Vector Access: Starburst unifies access to vector stores, enabling retrieval augmented generation (RAG) and search tasks across Iceberg, PostgreSQL + PGVector, Elasticsearch and more. Enterprises gain flexibility to choose the right vector solution for each workload without lock-in or fragmentation.
- Model Usage Monitoring & Control: Starburst offers enterprise-grade AI model monitoring and governance. Teams can track, audit, and control AI usage across agents and workloads with dashboards, preventing cost overruns and ensuring compliance for confident, scalable AI adoption.
- Deeper Insights & Visualization: An extension of Starburst’s conversational analytics agent enables users to ask questions across different data product domains and provide back a natural language response in natural language, a visualization, or combination of the two. The agent is able to understand the user intent and question to do data discovery to find the right data before query processing to answer the question.
Beyond Dashboards and Copilots: The Next Era of AI
AI is rapidly moving past dashboards and copilots toward autonomous workflows that demand both real-time decisioning and long-term context. For enterprises in regulated sectors, including finance, telecom, manufacturing, and public services, this shift raises a critical challenge: how to harness AI’s potential without compromising on data sovereignty,governance, or compliance.
“Starburst’s federated approach eliminates the need to centralize data while delivering consistent policy enforcement and transparent lineage,” said Matt Fuller, VP of AI/ML Products at Starburst. “This means companies operating across European borders can confidently build AI and agentic workflows without compromising compliance. Our AI-ready lakehouse is designed with privacy, trust and performance at its core, giving teams governed access to the data that matters, whether they’re training LLMs, deploying retrieval-augmented generation, or orchestrating multi-agent workflows, without limitations of legacy architectures or vendor lock-in.”
Starburst’s Platform: Built for Global-Scale, Compliance-First AI
Building on its core capabilities, Starburst enables enterprises operating across the EU and other regulated regions to deploy AI without breaching data residency, privacy, or compliance mandates. The platform provides federated access to distributed data, allowing organizations to query and analyze information in place without unnecessary movement.
By design, Starburst ensures data sovereignty across borders, clouds, and business units, while metadata-driven policy enforcement supports GDPR, Schrems II, and other evolving global regulations. With governance embedded at every layer, enterprises gain the confidence to scale AI securely and compliantly, no matter where their data lives.
