Zoho adds AI agents to analytics platform

Three call center agents working diligently with headsets in an office setting, showcasing teamwork. Photo: MART PRODUCTION/Pexels

Zoho is expanding its Analytics platform with a new generation of AI agents aimed at turning raw data into actionable decisions. The update spotlights Ask Zia, a conversational assistant that now sits at the core of employee interaction with business-intelligence tools.

Instead of moving through dashboards or waiting for analysts, users can type natural-language questions and let the AI agents handle the analysis. The system parses the query, selects the appropriate tool and returns a response without the need for manual report building.

For instance, a company owner could ask Zia to investigate a dip in sales, surface relevant trends, create a chart and even trigger a workflow based on the findings—all through a single conversation.

“We are at the beginning of the next leap forward in data analytics, where a strong data foundation is the starting point for all agentic activity in business operations,” said Veerakumar Natarajan, Country Head, Zoho Kenya.

At the heart of the rollout is a multi-agent architecture. Rather than relying on a single large language model, the platform distributes tasks among specialized agents that focus on data preparation, visualization, modeling, coding and action execution.

How the multi-agent system works

Two new layers help the agents understand the specific business they serve. The semantic layer creates a shared glossary of terms, metrics and definitions, preventing confusion when the same word means different things across organizations. The ontology layer goes further by mapping relationships among entities such as customers, products and departments, giving the AI a contextual map of how parts of the business fit together.

In many ways, this mirrors earlier moves in enterprise software where tools shifted from static reporting to dynamic assistance. Just as spreadsheet add-ons once promised automation but required careful setup, today’s AI agents aim to blend ease of use with deeper integration, though success still hinges on data quality.

Model flexibility and external integrations

The company does not lock the platform to its own large language model. Customers can choose the built-in Zoho LLM or configure external models, though the organization has not disclosed which functions each model handles.

Through the Model Context Protocol, third-party assistants like Claude can access Zoho Analytics data. This opens the door for users to work with analytics from within their preferred AI chat environments.

Supporting the expanded capabilities are more than 30 new data connectors added to the existing catalog of over 500, covering Zoho’s own apps and external services such as SAP, PayPal, Adobe Commerce, Twilio and Notion.

Privacy and administrative oversight

Zoho states that customer data is not used to train its AI models. However, when an external service like Claude processes a query via the MCP server, the query results may be shared with that provider under its own policies.

Administrators retain control over which AI features are enabled, what data agents can access and whether high-impact actions require user approval. The platform also offers observability tools that log which agents ran, who invoked them and what information was accessed.

Custom agents and contextual memory

Businesses can create agents that draw on proprietary data sets, internal processes and knowledge bases, tailoring AI behavior to unique operational needs.

Developers use a visual studio within Zoho to define data sources, set business rules and embed domain-specific logic, allowing agents to act on familiar terminology.

Conversation history is retained by Zia, enabling follow-up queries to reference earlier steps without re-entering parameters, which streamlines multi-stage investigations.

Agent memory stores intermediate results, so a user asking for a sales trend can later request a related profitability analysis without repeating the initial filter.

Testing, version control and observability

Zoho provides sandbox environments where teams can trial new agents and analytics assets before they reach production, reducing risk of unintended actions.

Version management tools let administrators roll back to previous configurations, preserving stability when updates introduce errors or unexpected behavior.

Data lineage features trace the origin of each data point, showing how transformations affect downstream reports and allowing impact analysis of model changes.

Cost tracking panels break down resource consumption by team, helping organizations allocate budgets and monitor AI usage across departments.

These controls support governance policies, ensuring that high-impact operations such as record modifications or workflow triggers require explicit approval.

The company’s approach reflects a broader industry move toward AI that not only answers questions but also participates directly in business processes.