STRATEGY NAMED AS A REPRESENTATIVE VENDOR IN THE 2026 GARTNER® MARKET GUIDE FOR AGENTIC ANALYTICS.

Quick Answer
Agentic analytics becomes enterprise-grade when AI agents operate on governed semantic context, not raw schemas and one-off integrations.

Strategy has been named as a Representative Vendor in the 2026 Gartner® Market Guide for Agentic Analytics. In our view, this inclusion reflects a broader market shift toward governed, semantically grounded analytics automation.

Strategy has been named as a Representative Vendor in the Gartner® Market Guide for Agentic Analytics.

Strategy, formerly MicroStrategy, is focused on making intelligence a core operating capability for every enterprise. That means analytics must be governed, embedded into workflows, and trusted enough to support action.

As AI agents move from experimentation into enterprise analytics workflows, the foundation matters. Agents need more than access to data. They need governed semantic context, inherited security, and operational controls so answers can be trusted, explained, reused, and scaled.

THE CONTEXT GAP
The distance between what an AI agent is asked versus what it actually knows.

Without a governed semantic layer, enterprise AI agents frequently lack the context they need to return trusted answers. A common failure mode is not the language model itself. Often, the agent does not know:

• Which metric definition is trusted
• Which data source is authoritative
• Which permissions apply to a given user or workflow
• How a business term changes across regions, products, and time periods

Closing the context gap requires more than connecting an LLM to a data warehouse. It requires trusted business context that travels with every query, workflow, and agent interaction. The market is moving from chat over dashboards to governed analytics automation.

Enterprise agentic analytics does not scale when agents are forced to reason over raw tables, inconsistent KPIs, and one-off APIs. It scales when semantic meaning, security, and policy are inherited by design.

Governed semantic context provides AI agents with a stable business layer above changing schemas, clouds, and tools. It also makes governance operational rather than reactive, with monitoring, usage controls, and auditability built into how analytics automation runs.

As agentic usage grows, reusable semantic objects and governed execution paths also help enterprises manage performance and cost more predictably.

Strategy Mosaic is Strategy’s universal semantic layer, designed to give AI agents, BI tools, and applications access to governed metrics, business-native objects, inherited permissions, and reusable semantic context across heterogeneous data sources.

MCP-enabled access provides a more standardized way to extend that context into AI tools, reducing reliance on one-off integrations. Rather than exposing agents directly to raw schemas and brittle point integrations, Mosaic provides governed semantic objects that travel across tools, clouds, and AI environments.

For AI tools and workflows that do not use MCP, Strategy Mosaic also supports direct REST API access, meaning governed semantic context is reachable from any environment that can make an HTTP call, not just MCP-native clients.

Capabilities supporting governed agentic analytics

For enterprise buyers, this combination matters because agentic analytics must be governed, explainable, and cost-aware before it can move from demos into production workflows.

ATTRIBUTION & LEGAL DISCLAIMERS

^{Gartner, Market Guide for Agentic Analytics, Deepak Seth, Georgia O'Callaghan, Fay Fei, Jeroen Cornelissen, 9 February 2026.}

^{Gartner is a trademark of Gartner, Inc. and/or its affiliates.}

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