ServiceNow Unveils Real-Time Data Foundation for Autonomous AI

At Knowledge 2026, the annual customer and partner event held in Las Vegas, ServiceNow introduced a suite of data capabilities aimed at solving a persistent enterprise problem: AI agents that can recommend but cannot execute. The company's new Real-Time Data Foundation combines Context Engine, Autonomous Data Analytics, and Workflow Data Fabric to deliver live, governed intelligence directly into business workflows.

Most enterprise AI deployments stumble not on model quality but on data fragmentation. According to the official press release, disconnected systems and ungoverned data at critical decision points produce shallow intelligence. ServiceNow's approach grounds every AI decision in real-time operational context by integrating configuration management database (CMDB) data, workflow signals, analytics insights, and third-party systems under a semantic layer.

Context Engine maps every person, role, asset, service, and policy across a business in real time. This gives AI the institutional business context that only comes from being embedded in how a business actually operates. As the engine learns continuously from system activity, that intelligence compounds with every workflow, making AI more accurate the more it runs (a problem that has plagued users for years, frankly).

Autonomous Data Analytics, fueled by innovation from the recently acquired Pyramid Analytics, allows any person or AI agent to query the entire enterprise data estate in plain language and receive secure, contextual insights immediately. The system addresses the reality that dozens of disconnected systems, catalogued inconsistently or not at all, contribute to AI that can offer advice but cannot provide workflow resolution.

Three interconnected capabilities 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. Workflow Data Fabric with ServiceNow Otto makes all this accessible through a natural language experience that guides curated, governed data asset creation step by step. ServiceNow Data Catalog provides end-to-end visibility through automated discovery, lineage tracking, and a shared business glossary.

The physical experience of interacting with these systems matters. When an analyst clicks through a dashboard, they're no longer waiting for batched reports or wrestling with ETL pipelines. Live Connect capabilities give analytics providers direct access to live operational data without pipelines, data copies, or latency. The same database handles both operational and analytical workloads simultaneously, delivering real-time insights with no performance trade-offs and no separate infrastructure.

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. 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 across manufacturing, healthcare, and critical infrastructure.

Through the Workflow Data Network, ServiceNow is extending its ecosystem to include partners across Data Quality, Data Observability, and Data Security and Privacy. 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.

The announcement coincides with expanded collaboration between ServiceNow and NVIDIA. At the opening keynote, NVIDIA founder and CEO Jensen Huang joined ServiceNow chairman and CEO Bill McDermott to discuss the next phase of enterprise AI. The companies are expanding their collaboration across the full stack, delivering specialized autonomous AI agents powered by NVIDIA accelerated computing and open models.

ServiceNow is introducing Project Arc, a long-running, self-evolving autonomous desktop agent designed for knowledge workers, including developers, IT teams and administrators. Unlike standalone AI agents, Project Arc connects natively to the ServiceNow AI Platform through ServiceNow Action Fabric to bring governance, auditability and workflow intelligence to every action the autonomous desktop agent takes. It can access local file systems, terminals and applications installed on a machine to complete complex, multistep tasks that traditional automation cannot handle.

Project Arc uses NVIDIA OpenShell, an open source secure runtime for developing and deploying autonomous agents in sandboxed, policy-governed environments. ServiceNow is building on and contributing to OpenShell to advance a common foundation for secure, enterprise-grade agent execution. With OpenShell, enterprises can define what an agent can see, which tools it can use and how each action is contained.

As Gaurav Rewari, executive vice president and general manager of Data and Analytics Products at ServiceNow, stated: "The enterprises winning the AI race are bringing trusted, contextual data directly into the workflows that run the business, giving teams and AI the insights to act with confidence." That's what ServiceNow is: the platform where insight meets every workflow, every transaction, every decision, and each one compounds the intelligence that drives the next.

The architectural breakthrough underlying all three capabilities is RaptorDB Pro's engine. The same database handles both operational and analytical workloads simultaneously. This eliminates the traditional separation between transactional and analytical systems that has created latency and complexity for decades.

Whether users actually pay for it remains the real question. The technology addresses genuine pain points around data fragmentation and AI execution, but the market has seen similar promises before. Enterprises will need to see measurable ROI beyond pilot deployments to justify the investment in a comprehensive data foundation overhaul.

Artūras Malašauskas is an AI Systems Integrator with 20+ years of production-grade web engineering experience. He has designed, shipped, and scaled enterprise Python/PHP systems for logistics, SaaS, and public-sector clients. For the past year, he has focused exclusively on AI integrations: deploying open-source LLMs, building generative media pipelines (image, audio, video), and engineering multi-agent workflows for real production environments. His standard: reproducibility, security, cost-efficient inference—no vaporware. He documents and evaluates emerging AI tooling, separating verified capabilities from marketing noise. Technical editor at: muza-ai.eu, ai-verslas.lt, ai-naujinos.lt Connect on LinkedIn