SAS makes AI governance the centerpiece of its agent strategy

Enterprises are quickly moving from AI experimentation to deployment, however, when agentic AI begins making more decisions, invoking more tools, and operating across fragmented data environments, there can be an erosion of visibility, governance, and trust.

SAS laid out its answer to that problem at its annual conference, SAS Innovate, introducing a new family of copilots, agent frameworks, Model Context Protocol (MCP) plugins, and management tools to help enterprises operationalize AI without losing control of it.

“What we’re seeing here is really a shift from AI that forms to AI that acts,” Marinela Profi, the company’s global AI and generative AI market strategy lead, said at the event. “This is a significant leap, because it introduces new requirements around trust, around governance, around accountability.”

Interacting with agents more intuitively

To begin with, SAS today announced SAS Viya Copilot, a human-governed, conversational AI assistant embedded into its Viya platform. It integrates Microsoft Foundry, operating within analytics workflows to help developers, data scientists, and other users instructing it in natural language to analyze data, build models, and make decisions across workflows.

“You have an expert assistant that allows you to take actions, ask questions, and help you navigate across the full analytical lifecycle,” Profi explained.

Its capabilities include: General Q&A across core Viya applications; production of documented and explainable AI-generated code; model pipeline guidance including recommendations and next steps; conversational dashboarding; and visual investigation with AI-assisted search and alert narratives. Copilot capabilities will eventually extend to data management, model management, and AI infrastructure, according to SAS. 

The company is initially launching two Copilots: Asset and Liability Management (ALM), for developing scenarios, executing and interpreting financial risk workflows, and translating natural language inputs into analytic models; and Health Clinical Data Discovery, for analyzing data, creating cohorts, and investigating research papers and other medical documents.

SAS plans to expand Viya Copilot into additional industries, including banking and manufacturing, later this year.

Going beyond embedded AI assistants, SAS is providing tools and infrastructure to connect and govern internal and external agents. The new SAS Viya MCP server standardizes connections so external agents can safely access SAS tools, data, and models, using the large language model (LLM) or interface of their choice (Claude, GPT, Gemini), without having to create custom integrations, duplicate logic, or bypass controls.

“The Copilot is not only answering questions for you, it can invoke capabilities across Viya in a more structured way,” Profi said.

In addition, a new Agentic AI Accelerator provides a collection of code, interfaces, components, and best practices that allow teams across skill levels (developers, low-code or no-code users) to design, build, deploy, and manage agents within SAS Viya, she explained.

Current Viya users can access both the MCP server and AI Accelerator via GitHub.

Maintaining human judgment

SAS continues to emphasize the importance of oversight, trustworthy AI, and human-in-the-loop control.

Furthering this mission, the company is introducing SAS AI Navigator. The Software-as-a-Service (SaaS) tool helps enterprises inventory, govern, and apply policies to underlying AI models.

Available in Q3 2026 on Microsoft Azure Marketplace, the platform will offer an end-to-end view of all AI models and tools in use in an enterprise, whether built in-house or provided by third parties. Using it, enterprises will be able to apply internal policies and external regulations and frameworks to AI use cases.

“It’s giving visibility into your AI inventory,” Reggie Townsend, VP of SAS’ data governance and ethics practice, said at today’s event. “But it also answers the really basic question: How are we doing?”

Enterprises want “enough data at a glance” to consider tension points when they’re juggling factors like reputation, efficiency, and cost, he pointed out. They’re also viewing trust as a new business differentiator, even as a currency. 

Navigator started with a really simple idea, he noted: “What happens if we can make being responsible irresistible?” AI governance is one way to preserve human judgment amidst what he called “tech asymmetry.”

Technology unevenness has been a long-standing problem; While there’s strong technical capability, enterprises struggle to adapt to the pace of change at scale. “What folks need to do is try to translate some of these capabilities into a sustainable business advantage,” said Townsend.

As AI capabilities (and offerings) continue to expand, he urged users to gain “sufficient literacy,” approach AI with curiosity, and think critically about how evolving tools can apply to both business and personal life.

“In an emerging landscape like this, we’ve got to suspend certainty,” he said. “Certainty breeds rigidity, and rigidity suspends this idea of nuanced judgment, which we need right now.”

The next chapter of AI is about scaling that judgment, governing at speed, and turning trust into that competitive advantage, he emphasized.

Getting to the right enterprise data

Enterprise data can be fragmented across many different ecosystems (on-prem, in legacy infrastructure, or in private or public clouds), noted SAS industry market lead Alyssa Farrell. Beyond that, she said, “[enterprises] have low trust in the data itself, which is leading to low trust in decisions.” Further, performance constraints can hamper AI progress.

To address these issues, SAS today announced a targeted refresh of SAS Data Management, its cloud-native portfolio built on the Viya platform, adding or expanding its AI-ready data management, governance by design, agentic AI and copilots, and cloud-native analytic acceleration. It provides lineage, transparency, and control capabilities within workflows where data is accessed, prepared, and activated, Farrell explained.

“Agents and AI crave data more than ever before,” she said. “It’s really important that organizations get this right from the beginning, especially if they’re adding automation to that decision process.”

The re-architected platform grounds AI in trusted data, making raw data assets usable for AI. Notably, it brings analytics and AI to the data itself through SpeedyStore, the company’s cloud-native analytical data platform, negating the need to move volumes of data for processing, Farrell explained. Enterprises still retain digital sovereignty and can control workflows across their various data stores.

“We’re making sure our customers have everything they need to meet this moment [and] tools that access the data, manage the data and gain value from it,” Farrell noted. “They can really proceed at scale to operationalize AI with confidence.”