ServiceNow launched new data capabilities that put autonomous AI to work on live, governed enterprise intelligence. The announcement helps resolve the data fragmentation siloed across systems that has held enterprise AI back, and delivers the live intelligence, execution, and agent governance that autonomous AI requires. Most enterprise AI fails not because the models are flawed, but because the data is fragmented across disconnected systems and ungoverned at the exact points where AI agents need to act, producing shallow intelligence that recommends rather than executes.
ServiceNow's Context Engine and Autonomous Data Analytics change the equation by drawing from the full breadth of enterprise signals (including assets, workflows, people, policies, operational history), and applying a semantic layer that integrates CMDB, workflow data, analytics insights, and third-party systems to ground every AI decision in real-time operational context. As Context Engine learns continuously from system activity, that intelligence compounds with every workflow, making AI more accurate the more it runs. Context Engine provides the deep context and governance that enterprise AI requires.
It maps every person, role, asset, service, and policy across a business in real time, giving AI the institutional business context that only comes from being embedded in how a business actually operates. To feed the Context Engine with trusted business logic, ServiceNow is announcing a new vision for Autonomous Data Analytics. Fueled by the innovation from recently acquired Pyramid Analytics, any person or AI agent can query the entire enterprise data estate in plain language and receive secure, contextual insights immediately.
Dozens of disconnected systems, catalogued inconsistently or not at all, and governed through processes designed for human analysts rather than AI agents, contribute to AI that can offer advice but can?t provide workflow resolution. ServiceNow is addressing this with three interconnected capabilities that connect data discovery, governance, and autonomous action without ever leaving the platform where work gets done. Autonomous Data Governance continuously monitors the data estate and automatically flags quality violations, helping enforce security and privacy policies in real time so the data feeding AI workflows always meets defined standards without manual intervention.
And Workflow Data Fabric with ServiceNow Otto makes it all accessible to any user through a natural language experience that guides curated, governed data asset creation step by step. ServiceNow Data Catalog gives organizations end-to-end visibility across their entire data estate through automated discovery, lineage tracking, and a shared business glossary. That foundation integrates with existing data catalogs across the enterprise, so organizations can gain faster discovery, deeper context, and broader adoption without replacing what they already have.
The result is a single, governed review of the entire data estate, wherever that data lives. As agentic workloads demand real-time access, long-term retention, and analytical flexibility, ServiceNow is expanding RaptorDB Pro, the high-performance database native to the ServiceNow AI Platform. Live Perform extends analytical processing to meet the scale of agentic workloads, building on the performance and scalability gains that have driven RaptorDB Pro adoption over the past several quarters.
The architectural breakthrough underlying all three capabilities is RaptorDB Pro?s engine: the same database handles both operational and analytical workloads simultaneously, delivering real-time insights with no performance trade-offs and no separate infrastructure. Live Connect capabilities give Pyramid Analytics and other analytics providers in the Workflow Data Network direct access to live ServiceNow operational data without pipelines, data copies, or latency. Live Archive lets historical and live data be queried together from cost-optimized storage, so long-term compliance and real-time performance no longer compete.
RaptorDB Pro also adds native support for multi-modal processing of graph and time-series data, powering complex context modeling and new use cases across manufacturing, healthcare, and critical infrastructure. Workflow Data Fabric extends this execution layer across the entire enterprise data estate, giving organizations the flexibility to choose best-of-breed data management partners without vendor lock-in. Through the Workflow Data Network, ServiceNow is extending its ecosystem to include partners across Data Quality, Data Observability, and Data Security and Privacy, enabling these solutions to push rich contextual intelligence directly into workflows, surfacing data quality and observability health indicators, sensitive data classifications, and governance policies at the exact point where they become actionable.
The new Workflow Data Network Partner Passport makes procurement seamless: customers use existing Data Fabric credits to activate and consume select partner solutions from qualified partners, starting with IBM and Boomi, consolidating data, AI, and workflows under a single commercial agreement. A new governance gap is opening as agentic AI adoption accelerates: AI agents are connecting to external services, MCP servers, and data sources with no central oversight, no approval workflow, and no audit trail. Enterprises that rigorously govern how employees access sensitive systems are, in many cases, applying no equivalent controls to the agents now acting on their behalf.
The ServiceNow MCP Registry is a private enterprise Model Context Protocol registry.
