Fueling AI

As enterprises increase their investments in AI development but frequently struggle to move AI initiatives past experimentation and into production, feeding agents and other AI tools the high-quality, relevant data they require to properly perform has been a common hurdle.

In response, data management and analytics vendors such as Databricks, MongoDB and Tableau have prioritized providing tools that help customers discover and deliver contextually appropriate data to AI tools.

These new features provide an agentic AI framework for Alteryx's users, which is important given the focus on AI in today’s market. In addition, there is a revival of interest in on-premises capabilities both for governance reasons and to address cost concerns. Several of these features address those concerns.

With Agent Studio and its MCP Server, Alteryx is similarly adding capabilities designed to help customers deliver trusted, relevant data to AI-powered systems in a move motivated by a combination of customer feedback and first-hand experience building agents, according to Alteryx CEO Andy MacMillan.

"A lot of the AI capabilities, the idea that we want to have visible, trusted, auditable data in agents, has come from first-hand experience … being business analysts trying to bring data to AI," he said.

However, by making Agent Studio and MCP Server part of Alteryx One -- a low-code/no-code platform for data management and insight generation -- Alteryx is taking a different approach to AI development than many other data management and analytics vendors. Instead of creating a development environment for centralized IT teams, it is empowering business users to build agentic AI tools.

Beyond the first-hand experience MacMillan cited, an Alteryx survey of more than 1,400 business leaders showed that 11% of respondents expect responsibility for AI workflows to move to line-of-business domains over the next three years.

"Agent Studio, MCP Server and a lot of the things we're talking about are designed around how to make AI trusted, and how to make it trusted is by empowering Alteryx users -- the business analyst -- to be the one to connect enterprise data, business logic and governance in a way that the business can depend on," McMillan said.

Donald Farmer, founder and principal of TreeHive Strategy, noted that Alteryx is taking a novel approach by empowering business users to build AI workflows. However, while Alteryx is now providing the AI development capabilities it needs to remain viable, its approach is questionable, he continued. 

"The work on an MCP server is necessary," he said. "Alteryx needs this capability to remain credible. Its historical differentiation has been business logic captured in the workflow. Exposing that through MCP is a coherent move. Whether enterprises want to route their [large language model] traffic through Alteryx workflows rather than building pipelines elsewhere is an open question."

In addition, basing a strategy on 11% of organizations expecting to decentralize agent development management could prove dubious, according to Farmer.

"That's not a transformative number," he said. "In fact, it is well within the margin of simple organizational drift."

Menninger, however, countered that Alteryx has built a differentiated business over the years that the empowerment of business users builds upon. Alteryx's main focus has been data preparation. In addition, however, it provides analytics operations capabilities that ensure consistency and governance within analytics workflows.

"These new features bring Alteryx's unique capabilities to the world of agentic AI," Menninger said.

Specifically, they enable users to integrate deterministic Alteryx workflows established over time with probabilistic agent-based processes, he continued.

"By providing an agentic framework, Alteryx customers can more easily bring these two types of processes together," Menninger said.

Beyond Agent Studio and the Alteryx One MCP Server, new Alteryx capabilities include the following:

• An Alteryx One desktop app for users that prefer a desktop environment to the web.
• New deployment options including Workspace Execution so users can run workflows in the cloud, Data Bridge to enable cloud-based workflows to securely connect with on-premises and private network data without moving it into the cloud, and Server Execution so analysts can view and manage server-based workflows from the cloud while running them on premises.
• Live Query and new connectors that allow users to work with data where it lives rather than moving it into Alteryx.
• Data Labels and asset certification that show where data comes from, who within an enterprise is responsible for it, and how it is being used to inform data and AI initiatives.

Data Bridge and Server Execution -- which is not yet generally available -- are valuable additions, according to Farmer. However, he noted that by launching capabilities that enable workflow orchestration from the cloud before making Server Execution GA, Alteryx, while putting in time-consuming product development work, appears to be prioritizing its cloud business over its historical base of on-premises users.

"Cloud-managed orchestration of on-premises workflows is exactly what large customers have been asking for, [but] shipping the cloud-native execution path first suggests the cloud business is being prioritized over the existing customer footprint," he said.

Looking ahead

As Alteryx plans future product development, the shift from agentic coding to agentic building and further removing AI development from centralized teams are focal points, according to MacMillan.

Agentic AI tools such as Claude and ChatGPT can write code that helps Alteryx customers build agents. However, not all business users are experts in coding. The next step, therefore, is to enable large language models to not just write code, but build Alteryx workflows.

"I think that is coming, agentic building for non-coders into environments that make sense for them that they trust," MacMillan said. "That's a really big one for us."

Menninger, meanwhile, noted that enterprises struggle to integrate deterministic and probabilistic processes. Therefore, adding more capabilities that enable customers to combine the two would benefit existing users and perhaps appeal to potential new ones.

"Alteryx can help play a role in bringing these two worlds together by continuing to extend its agent-to-agent capabilities and supporting a mixture of those two types of activities," he said.

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.