I have recently written about the importance of context in relation to agentic artificial intelligence, including the growing use of Model Context Protocol to enable LLMs, agents and applications to communicate with data platforms, file systems and development and productivity tools, and the importance of semantic data modeling to provide agreed definitions that reflect the meaning of entities, facts, relationships and business logic. Context was a key focus of Domo’s recent customer event in Salt Lake City, with the company announcing new AI agent creation and data integration capabilities, as well as Project Ambience to deliver a new Intent Engine to support agentic memory for enterprise AI.
Domo was founded in 2010 by chief executive officer Josh James after he identified an opportunity to create a business intelligence platform designed to better enable CEOs and other senior executives to access enterprise data directly rather than relying on reports and dashboards created for them by data and analytics specialists. Expanding on that original vision over the past 16 years, Domo now provides a platform for self-service BI and AI that addresses data integration, data processing, data governance and application development, as well as data science, machine learning and generative and agentic AI. The company was rated as Exemplary in ISG Research’s 2025 Analytics Buyers Guide and the associated Buyers Guides for Collaborative Analytics, Developer Analytics, Mobile Analytics and AI Analytics—for which Domo was ranked as the overall leader. The company was also rated as Exemplary in the 2025 ISG Buyers Guides for Data Integration and Data Products, and was rated a provider of Assurance in the 2026 Buyers Guide for AI Platforms, as well as AI Governance and Operations and Agentic and Generative AI. Domo claims more than 2,600 customers and recently reported fiscal 2026 revenue of $319 million.
As I explained last year, the company unveiled Agent Catalyst at its 2025 Domopalooza event to enable the creation of AI agents based on a four-step process of model selection, instruction definition, knowledge provision and tool selection. At this year’s event, the company expanded its agentic AI capabilities with the addition of AI Agent Builder, AI Toolkits, a centralized AI Library and the Domo MCP Server. The Domo AI Library is designed to provide a central hub for curating and managing AI initiatives and includes AI Agent Builder to enable users to create conversational agents or agentic workflows targeted at specific use cases. AI Toolkits are sets of reusable packaged capabilities that provide the instructions and business context to guide agent operations, while the Domo MCP Server utilizes Model Context Protocol to expose AI Toolkits to external AI tools and AI assistants and provide them with access to enterprise data within Domo. I assert that through 2028, almost all data-related software providers will adopt Model Context Protocol to provide interoperability between agentic applications and trusted enterprise data and business workflows.
The increased use of AI agents requires orchestration that utilizes AI to automate tasks and improve decisions, enabled by a context layer that captures established enterprise
Domo explained how Project Ambience is intended to provide a multi-tiered memory architecture that will combine episodic memory, semantic memory and procedural memory to ensure that agents are operating in the context of validated facts and reusable knowledge as well as skills, processes, policies and execution patterns in addition to real-time prompts, tools, actions and events. Further details of Project Ambience and Intent Engine will be rolled out in the future with no specific delivery roadmap at this stage, but Domo made numerous other product announcements at Domopalooza 2026 in relation to new or near-term product enhancements. The company unveiled enhancements to its data integration functionality including a redesigned Magic ETL authoring experience and a new AI Assistant for the JSON No Code connector designed to automatically generate configurations based on natural language prompts. Domo also announced Domo Documents (previously referred to as Filesets) to provide agents with context from unstructured data via retrieval augmented generation.
Also announced at Domopalooza 2026 were a spreadsheet-style experience called Worksheets, Data Models to define relationships between datasets once and reuse them across the Domo platform, as well as enhancements to Domo’s semantic layer. New administration enhancements include Native App Distribution to enable users to deploy Domo applications as standalone apps in the Apple App Store and Google Play, Global Personalized Data Permissions (PDP) policies that can be applied across multiple datasets and User Impersonation, which enables authorized administrators to interact with Domo as another user to improve the diagnosis of user permission and access issues. Earlier this year, Domo also introduced a new product feature called App Catalyst designed to accelerate the development of data-driven applications through the use of natural language prompts.
As indicated by the breadth and depth of the announcements, Domo should be thought of as providing much more than a business intelligence platform. Many of the product enhancements announced by Domo are in the early stages of availability, and it is not currently clear how and when the results of Project Ambience will be added to the platform. While potential customers should therefore be cognizant of delivery schedules, I recommend that enterprises evaluating providers for analytic, data and AI platform capabilities include Domo in their assessments.
Regards,
Matt Aslett