SALT LAKE CITY, Utah -- Domo is diving headlong into AI, going well beyond its business intelligence roots by adding a series of new capabilities to its platform aimed at making it easier for enterprises to develop agents and other AI applications using their proprietary data. After rebranding its capabilities 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 unveiled an AI Library where customers can curate and manage AI applications, AI Agent Builder to enable easy development of conversational agents, and AI Toolkits so that customers can combine data sources and AI workflows to define an agent's purpose. In addition, Domo introduced its Model Context Protocol (MCP) Server to connects the vendor's AI Toolkits and agents with external AI platforms including large language models such as OpenAI's ChatGPT, Gemini from Google and Anthropic's Claude. Beyond new capabilities aimed at enabling AI development, Domo also revealed new platform features including Worksheets, which has a spreadsheet-like manner of interacting with data with the Domo environment, and improvements to its semantic layer to help make data consistent across entire organizations and easy to discover for AI and analytics-driven insight generation. The new capabilities were introduced during Domopalooza, Domo's annual user conference in Salt Lake City, and are valuable for the vendor's users given that they build on Domo's previous AI capabilities such as workflow automation, according to Michael Ni, an analyst at Constellation Research. &#34;Agents and MCP, coupled with announced centralization of semantics, are about making their workflow and AI capabilities consistent, governed and reusable across the enterprise,&#34; he said. &#34;For customers, Domo is delivering on the promise of less fragmentation across data, workflows and AI; more decisions happening inside the system; and a path from data products to driving operational actions.&#34; Based in American Fork, Utah, Domo is an analytics vendor that, along with competitors such as Qlik and ThoughtSpot, has expanded beyond its historic focus to add capabilities that enable 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 Products Platform that allows developers to use natural language prompts in conjunction when building pro-code tools fed by Domo data. <section> <h2>Commitment to the cutting edge</h2> A bit more than three years ago, before OpenAI's November 2022 launch of ChatGPT marked significant improvement in generative AI (GenAI) technology and sparked surging interest in AI development that continues to grow, Domo was largely focused on enabling customer to develop and embed traditional data products such as dashboards and reports. <blockquote> <div> <figure> Agents and MCP, coupled with announced centralization of semantics, are about making their workflow and AI capabilities consistent, governed and reusable across the enterprise. For customers, Domo is delivering on the promise of less fragmentation across data, workflows and AI. </figure> <figcaption> Michael NiAnalyst, Constellation Research </figcaption> </div> </blockquote> By mid-2023, Domo had made GenAI a focal point of its platform, and that evolved in 2025 to include agentic AI. The latest additions to Domo's AI and Data Products Platform are designed to better enable customers to operationalize their AI initiatives because, despite heightened focus on AI development over the past few years, the majority of all AI projects never make it into production. With the failure rate of AI projects so high, vendors including Databricks, MongoDB, Snowflake and ThoughtSpot have all introduced new capabilities in 2026 specifically aimed at helping customers more successfully develop agents and other AI tools. Domo is doing the same with its latest AI and Data Products Platform capabilities which, given that they address the gap between trusted data in enterprise systems and intelligence in AI models, are valuable for Domo users, according to Donald Farmer, founder and principal of TreeHive Strategy. &#34;Most organizations that have experimented with AI assistants have run into this [problem] -- the model is capable, but it is working with stale, unverified data or information that bypasses the access controls the organization spent years building,&#34; he said. &#34;Domo wants to close that gap, at least within its own ecosystem.&#34; Specific new features of Domo's AI and Data Products Platform include the following: <ul type="disc"> <li>The Domo AI Library, a centralized hub within the platform where users can curate and manage their AI tools.</li> <li>AI Agent Builder, a framework for developing conversational agents and agentic workflows tailored to specific tasks such as customer service or supply chain management.</li> <li>AI Toolkits, frameworks that enable users to define how agents call on external tools for information, access proprietary data and conduct workflows so that agents can take on roles that assist workers and relieve them of certain previously manual tasks.</li> <li>An MCP server to securely connect AI Toolkits and agents with AI models in a repeatable manner so developers don't have to configure connections each time they build a new application.</li> </ul> Additional new Domo platform features not specifically designed to help customers build agents and other AI applications include Worksheets, Report Builder for PDF to simplify producing formatted executive reports, Data Models so users can define semantic relationships between datasets and Inline Chart Editor to enable users to edit visualizations directly on their dashboards. The MCP server, which enables agents to access the data that gives them proper context, is perhaps the most significant of the new features, according to Ni. Meanwhile, by adding the new features, Domo is providing more advanced agentic AI development capabilities than some of its traditional BI competitors, Ni continued. However, compared to broader data platform vendors, Domo's AI capabilities are not differentiated. &#34;Domo has been early … compared to traditional BI vendors, but they're not setting the broader market agenda,&#34; he said. &#34;As the market shifts toward AI-driven execution, Domo has long had pieces in place. The question now is whether Domo evolves into a true control layer or remains a complementary capability within the evolving data and AI stack.&#34; Farmer likewise named the MCP server as the most valuable of Domo's new platform capabilities. &#34;The MCP Server turns Domo into a data and workflow provider for whatever AI assistant the user or organization already prefers,&#34; he said. &#34;For users, it means they can interact with Domo data and trigger Domo workflows from within their preferred AI assistant, without navigating Domo's own interface.&#34; Regarding Domo's competitive standing, Farmer added that &#34;the picture is mixed.&#34; He noted that while there are some financial struggles, the vendor's &#34;technology is excellent and the technical strategy sound.&#34; <figure class="main-article-image full-col"> <figcaption> Josh James, CEO of Domo, delivers the keynote address during the vendor's annual Domopalooza user conference. </figcaption> <div> </div> </figure></section> <section> <h2>Looking ahead</h2> After introducing its latest swath of AI and Data Products Platform capabilities, Domo should focus on making its existing tools easier for customers to operationalize, according to Ni. Adding more functionality is wise, he noted. But it's also important to ensure that such functionality leads to tangible results. &#34;The next step is packaging data, workflows and agents into repeatable solutions that deliver measurable outcomes,&#34; Ni said. Farmer, meanwhile, suggested that Domo add governance capabilities that address multi-agent systems rather than individual agents. &#34;They should look at the problem of emergent failures in multi-agent systems,&#34; he said. &#34;Individual agents may each behave correctly within their own scope, but two agents, each locally rational, may produce a globally damaging outcome because neither had visibility into the other's decision loop.&#34; Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</section>

DOMO

Domo is enhancing its AI capabilities with new features in its Data and AI Products Platform, aimed at helping enterprises develop AI applications using proprietary data. Key additions include the Domo AI Library for managing AI tools, an AI Agent Builder for creating conversational agents, and AI Toolkits for integrating data sources. The Model Context Protocol (MCP) Server connects Domo's tools with external AI platforms. These updates, revealed at Domopalooza, focus on reducing data fragmentation and improving operationalization of AI initiatives, addressing common challenges in AI project implementation.

Domo doubles down on AI with latest platform additions