SALT LAKE CITY, Utah — Domo is going well beyond its business intelligence roots by taking AI head-on and adding a series of new features to its platform aimed at making it easier for businesses to develop agents and other AI applications using their own data.
After rebranding its capabilities to the Domo Data and AI Products Platform and calling it a centralized environment for building and managing agents, the one-time analytics specialist on Wednesday announced an AI library that allows customers to curate and manage AI applications, an AI Agent Builder that enables easy development of conversational agents, and an AI toolkit that allows customers to combine data sources and AI workflows to define agent objectives.
Additionally, Domo introduced a Model Context Protocol (MCP) server to connect vendor AI toolkits and agents to external AI platforms, including large language models such as OpenAI’s ChatGPT, Google’s Gemini, and Anthropic’s Claude.
In addition to new features aimed at enabling AI development, Domo also unveiled new platform features such as worksheets, a spreadsheet-like way to work with data in the Domo environment, and improvements to the semantic layer that ensure data consistency across the organization and make it easier to discover data for AI and analytics-driven insight generation.
Constellation Research analyst Michael Ni said the new feature, introduced at Domo’s annual user conference Domopalooza in Salt Lake City, is valuable to the vendor’s users given that it builds on Domo’s previous AI capabilities, such as workflow automation.
“Agents and MCPs, coupled with the announced centralization of semantics, aim to make workflows and AI capabilities consistent, manageable, and reusable across the enterprise,” he said. “For customers, Domo promises a path to reduce fragmentation across data, workflows, and AI, make more decisions within their systems, and drive operational actions from data products.”
American Fork, Utah-based Domo, along with competitors such as Qlik and ThoughtSpot, is an analytics vendor that is moving beyond its previous focus to add capabilities that allow customers to develop and deploy agents and other AI applications. In January, the vendor launched App Catalyst, a tool within the Domo AI and data product platform. This allows developers to mix and match natural language prompts when building pro-code tools powered by Domo data.
Commitment to the cutting edge
A little more than three years ago, before OpenAI announced ChatGPT in November 2022, significantly improving Generative AI (GenAI) technology and sparking interest in growing AI development, Domo was primarily focused on enabling customers to develop and embed traditional data products such as dashboards and reports.
Agents and MCPs, combined with the announced centralization of semantics, aim to make workflows and AI capabilities consistent, manageable, and reusable across the enterprise. Domo is delivering on its promise to customers to reduce fragmentation across data, workflows, and AI.
michael neeConstellation Research Analyst
By mid-2023, Domo had GenAI at the heart of its platform, which evolved to include agent AI in 2025.
The latest additions to Domo’s AI and data product platform are designed to help customers better operationalize their AI initiatives, as despite the increased focus on AI development over the past few years, the vast majority of all AI projects never reach production. Because AI project failure rates are so high, vendors like Databricks, MongoDB, Snowflake, and ThoughtSpot have all introduced new features in 2026 specifically aimed at helping customers develop agents and other AI tools more successfully.
Donald Farmer, founder and principal at TreeHive Strategy, says Domo is doing the same with its latest AI and data product platform capabilities, which are valuable to Domo users as they address the gap between trusted data in enterprise systems and the intelligence of AI models.
“Most organizations that have experimented with AI assistants have run into this problem. [problem] “While this model is capable, it deals with old and unverified data and information that circumvents the access controls that organizations have spent years building,” he said. “Domo wants to close that gap, at least within its own ecosystem.”
Specific new features of Domo’s AI and data product platform include:
The Domo AI Library is a centralized hub within the platform where users can curate and manage their AI tools.
AI Agent Builder is a framework for developing conversational agents and agent workflows tailored to specific tasks, such as customer service or supply chain management.
AI toolkits are frameworks that allow users to define how agents invoke external tools for information, access proprietary data, and execute workflows, allowing agents to take on the role of assisting employees and freeing them from certain tasks traditionally performed manually.
MCP Server for securely connecting AI toolkits and agents to AI models in a repeatable way. Developers don’t have to configure connections every time they build a new application.
Additional Domo platform features not specifically designed to help customers build agents and other AI applications include worksheets, a report builder for PDF that simplifies the creation of formatted executive reports, a data model that allows users to define semantic relationships between datasets, and an inline chart editor that allows users to edit visualizations directly on dashboards.
The MCP server, which gives agents access to data that provides the right context, is perhaps the most important new feature, according to Ni.
Meanwhile, by adding new features, Ni continued, Domo offers more advanced agent AI development capabilities than traditional BI competitors. However, compared to a broader range of data platform vendors, Domo’s AI capabilities are undifferentiated.
“Domo came out early compared to traditional BI vendors, but it hasn’t set the broader market agenda,” he said. “Domo has long had the right pieces as the market moves towards AI-driven execution. The question now is whether Domo will evolve into a true control layer or continue to be a complementary function within an evolving data and AI stack.”
Farmer similarly cited the MCP server as Domo’s most valuable new platform feature.
“MCP Server turns Domo into a data and workflow provider for all the AI assistants that users and organizations already love,” he said. “For users, this means they can interact with Domo data and trigger Domo workflows from within their favorite AI assistant without having to interact with Domo’s own interface.”
Regarding Domo’s competitiveness, Farmer added, “It’s a mixed bag.” He noted that despite the financial difficulties, the vendor’s “technology is good and its technology strategy is sound.”
Looking to the future
After introducing the latest AI and data product platform capabilities, Domo should focus on making it easier for customers to operate their existing tools, according to Ni.
He pointed out that it would be wise to add more features. However, it is also important to ensure that such features lead to tangible results.
“The next step is to package data, workflows, and agents into repeatable solutions that deliver measurable outcomes,” Ni said.
Meanwhile, Farmer suggested adding governance features to Domo that address multi-agent systems rather than individual agents.
“We should consider the issue of sudden failures in multi-agent systems,” he said. “Individual agents may behave correctly within their own limits, but two locally rational agents can produce collectively harmful outcomes because neither is aware of the other’s decision-making loops.”
Eric Avidon is a senior news writer at Informa TechTarget and a journalist with more than 30 years of experience. He is responsible for analysis and data management.
