Domo unveils AI agent builder to connect enterprise data with external AI platforms

Domo Introduces AI Agent Builder and Orchestration Framework

Business intelligence software firm Domo announced a comprehensive AI orchestration framework this week at its annual Domopalooza conference in Salt Lake City, featuring an AI agent builder designed to help organizations deploy custom AI agents integrated with enterprise data and business workflows1

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. The platform aims to operationalize AI by transforming it from an abstract capability into a system that drives tangible business outcomes.

"AI doesn't become valuable when a model gets smarter," said Domo founder and CEO Josh James. "It becomes valuable when it's connected to your business and becomes a system of action."1

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The announcement reflects a strategic shift toward making AI agents practical tools that connect enterprise data to AI ecosystems rather than standalone technologies.

Enterprise Data Connectivity Through Model Context Protocol

At the core of Domo's new offering is enterprise data connectivity powered by the industry-standard Model Context Protocol, which supplies AI agents with data directly from external application programming interfaces and AI platforms1

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. The Domo MCP Server extends AI toolkits and agents to external platforms including OpenAI ChatGPT, Google Gemini and Anthropic Claude2

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Through MCP, AI assistants can query datasets and analytics, trigger workflows and automation, create dashboards and applications, and configure alerts and operational processes2

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. This capability allows organizations to centralize the creation and management of AI agents within a trusted and governed environment while integrating both internal company data and external sources1

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Building Proactive Agents That Automate Tasks

The Domo AI Library, slated for availability this summer, serves as a central hub for curating and managing AI systems2

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. Within the library, users can create conversational agents with specific roles or goal-oriented agents that complete tasks on demand or when triggered by events1

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For instance, an agent could monitor a customer database for contact updates. When a sales representative adds a new contact, the agent automatically gathers information from the web, company sources, trusted industry resources and other external data, then compiles everything into a report for sales, marketing or outreach teams1

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. This approach moves beyond reactive ask-and-respond models to create proactive agents that automate tasks and anticipate business needs.

AI Toolkit Defines Agent Capabilities and Context

The AI toolkit component provides a packaged set of capabilities that define what an agent can do, allowing users to combine tools, data and workflows with instructions and business context2

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. This includes operational logic and domain-specific knowledge that guide how agents operate within specific business environments.

Marcus Wilkins, lead data scientist at InformData LLC, a provider of background screening and identity intelligence services, explained the practical value: "By bringing our data and workflows into a single, connected environment, it gives us the control and context we need to build reliably. On top of that, we're using gen AI in decision layers within our workflows to determine what data to use and how results move forward."1

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Building an Intelligent Enterprise Operating Model

Domo's platform builds on a broader vision for an intelligent enterprise operating model where business users orchestrate data systems and AI agents to improve decisions and accelerate outcomes2

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. The framework positions AI agents as a coordination layer while keeping employees at the helm, with agents reporting to them rather than replacing human oversight1

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The business intelligence company, headquartered in American Fork, Utah, has been expanding beyond its traditional data visualization and interactive dashboards to encompass data integration, transformation, data science, machine learning and embedded analytics capabilities in recent years2

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. These latest announcements represent a significant step toward making AI a practical component of daily business operations rather than an experimental technology. Organizations should watch how the summer release performs in production environments and whether the MCP integration delivers on its promise to bridge enterprise data with leading AI platforms.