Zoho Analytics Launches Native Semantic Layers to Power Multi-Agent Business Intelligence

Zoho Analytics Restructures Around Native Semantic and Ontology Layers

Zoho Analytics has unveiled a major architectural overhaul that positions the platform as "the intelligence layer for businesses," according to Tony Thomas, CEO of Zoho USA1

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. The update restructures the business intelligence platform around four core pillars: trusted data and business context, agentic analytics and actions, intelligence everywhere, and governed AI1

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. Unlike competitors such as Snowflake, Databricks, and Microsoft Fabric, Zoho Analytics ships with native semantic and ontology layers built directly into the platform, eliminating the need for a separate cloud data warehouse1

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Understanding the Trusted Business Intelligence Layer

The semantic and ontology layers form the foundation of Zoho's trusted business intelligence layer, sitting between raw data sources and the agents or users querying them1

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. The ontology layer maps canonical business entities and their relationships, defining a company's organizational structure2

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. Above this, the semantic layer functions as a business glossary, defining what entities and metrics actually mean in business terms through synonyms, descriptions, metric calculations, sample questions, and the SQL that answers them1

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. Both layers can be built manually or through an AI assistant, and users can build and edit multiple semantic models without writing any code2

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. Clarence Rozario, Director of Product Management at Zoho BI and Analytics Platform, explained that "to trust an AI agent with operational decisions, it must inherently understand an enterprise's unique metrics, hierarchies, boundaries, and business vocabulary"2

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AskZia Multi-Agent System Powers Agentic Analytics

Zoho has transformed AskZia, its natural-language assistant since 2018, into a conversational BI super-agent built on a multi-agent architecture1

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. The system now orchestrates six specialized sub-agents covering data analysis, visualization, data preparation, modeling, coding, and SQL generation to handle complex requests from a single prompt1

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. A new custom agent builder allows enterprises to create their own agents grounded on chosen datasets or semantic models, with knowledge-base files, Model Context Protocol (MCP) connections, and tools for executing actions1

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. These agents deliver AI-driven insights and actions that go beyond analyzing data to directly triggering automated workflows through a workflow builder in Zoho Analytics2

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Expanded Data Connectors and Interoperability

Zoho Analytics now supports 500-plus native data connectors, with 30 new additions in this release1

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. The new connectors expand integrations to include SAP, PayPal, Adobe Commerce, Twilio, Notion, Intercom, Supabase, Amazon DocumentDB, and AlloyDB, alongside expanded Zoho integrations for Zoho ERP, Zoho Spend, Zoho FSM, Zoho Shifts, and Zoho Voice1

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. The platform also offers a zero-copy connection mode for data left in Snowflake, Databricks, and other systems1

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. To prevent vendor lock-in, Zoho declares compatibility with Apache Ossie, the open-source semantic exchange standard that allows organizations to define metrics and dimensions once and share them seamlessly across different BI tools and AI agents1

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Headless BI and Embedded Intelligence Capabilities

Zoho Analytics introduces headless BI capabilities through a Zoho Analytics MCP server available in remote and local options, a Claude plugin, and a command-line interface1

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. This allows AI agents or developers in other platforms to read data, build dashboards, or trigger analytics actions from outside Zoho entirely1

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. For embedded analytics, the platform now carries the full agentic capability set with white-labeling controls, enabling independent software vendors to embed agents in their own apps, portals, and products without displaying the Zoho brand1

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. Michalis Stoupakis, Head of Product (Supply) at Desquared, noted that the company empowers over 15,000 merchants with AI-powered actionable insights through contextually embedded reports1

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Governed AI Through Unified Data Catalog

The governed AI pillar introduces a unified data catalog that covers data sources, pipelines, metrics, agents, and models in one view, with lineage and impact analysis across upstream and downstream dependencies1

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. This provides administrators visibility into and control over how agents operate across the organization, helping manage data access and cost, reduce errors, and maintain control over how information is used by agents2

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Composability and Future Data Platform Plans

Sailas Sundaram, Head of Product Management at Zoho Analytics, emphasized that the platform's composability allows any single layer to be adopted on its own, with the rest of the enterprise stack kept in place1

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. Cloud updates are available now, but preview versions of pixel-perfect reporting, the ontology, and the semantic model will roll out globally over the next four to eight weeks1

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. Looking ahead, Zoho is building the Zoho Data Platform targeted for 2027, which will handle structured and unstructured data in a single layer and compete directly with Snowflake, Databricks, and Microsoft Fabric as the common data layer underneath enterprise applications and AI1

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. The emphasis on composability and competitive pricing positions Zoho Analytics as an accessible alternative in the evolving business intelligence landscape, particularly for organizations seeking workflow automation without the complexity of separate data warehouse infrastructure.