Stibo Systems is adding agentic AI directly to its master data management platform with AgentWorkx, a framework designed to help enterprises build, deploy and govern AI agents against trusted business data. The company is also introducing two ready-to-use agents aimed at automating data onboarding and product content workflows.
Enterprise AI agents are moving beyond chat interfaces and into business processes, but their usefulness depends heavily on the quality and governance of the data they can access. Stibo Systems is betting that master data management can provide that foundation with AgentWorkx, a new framework for deploying and governing AI agents inside its STEP platform.
The company announced AgentWorkx alongside a set of Stibo Systems Agents, giving customers two paths into agentic AI. Enterprises can use prebuilt agents for common master data management workflows or configure their own agents for business-specific requirements. Both approaches operate within STEP’s existing roles, permissions, governance and enterprise controls.
That distinction matters as companies attempt to move AI from experimentation into production. Gartner predicts that 33% of enterprise software applications will incorporate agentic AI capabilities by 2028, up from less than 1% in 2024, while at least 15% of day-to-day work decisions could be made autonomously through agentic AI by then.
Stibo Systems’ approach puts the data layer at the center of that transition. Rather than treating an AI agent as an independent application, AgentWorkx connects agent activity to the company’s existing master data environment, where information about products, customers, suppliers and locations can be managed through established governance processes.
The first release includes two agents. Upload Anything AGT is designed to bring structured and unstructured information into the MDM environment, with Stibo Systems positioning it as a way to shorten data onboarding processes. Content Optimizer Agent uses governed product attributes and business context to generate product descriptions, SEO content and marketing copy.
The second use case is particularly relevant to enterprises managing large product catalogs. Generative AI can produce content quickly, but output quality depends on whether the underlying product information is accurate, complete and consistent. Connecting content generation to governed master data could reduce the risk of agents producing copy based on outdated or incomplete product attributes.
AgentWorkx also includes an agent gallery, development studio, orchestration capabilities and governance tools. Customers can build agents using the same framework Stibo Systems says its own product and engineering teams use, while keeping those agents inside the controls of STEP.
That governance layer is increasingly important as enterprises move toward autonomous workflows. McKinsey’s 2025 State of AI survey found that 62% of respondents said their organizations were at least experimenting with AI agents, but nearly two-thirds had not yet begun scaling AI across the enterprise.
The gap highlights a broader challenge for enterprise AI: deploying an agent is relatively easy compared with making it reliable enough to act on business-critical information. Permissions, auditability, data lineage, integration and model controls become more consequential when software is allowed to make decisions or initiate actions without a human reviewing every step.
Stibo Systems is therefore positioning MDM as more than a centralized data repository. Its existing semantic data foundation, access controls, auditability and integrations become infrastructure for agentic AI, potentially allowing organizations to automate workflows without creating a separate layer of unmanaged or “shadow AI.”
The strategy also puts Stibo Systems into a growing enterprise AI market that includes platforms from Microsoft, Salesforce, Google and other technology providers building agent frameworks and AI-powered business applications. The competitive question is increasingly shifting from whether enterprises can deploy agents to whether those agents can operate safely against trusted organizational context.
Stibo Systems says it plans to expand its ready-to-use agent portfolio and give customers greater flexibility around preferred AI models. If that roadmap materializes, AgentWorkx could evolve from an MDM-specific automation layer into a broader control point for AI agents working with enterprise data.
For enterprises, the underlying proposition is straightforward: autonomous software is only as useful as the information and permissions behind it. As agentic AI moves deeper into operational systems, trusted master data may become as important to AI infrastructure as the models themselves.
Market Landscape
Agentic AI is moving toward enterprise workflows, but adoption remains ahead of large-scale deployment. Gartner forecasts that agentic capabilities will appear in one-third of enterprise software applications by 2028.
The data challenge is equally significant. McKinsey reports that AI use reached 88% of surveyed organizations in 2025, yet only 7% said AI had been fully scaled across their organizations.
This creates an opening for MDM platforms to become part of the enterprise AI control layer. As agents gain access to operational systems, data quality, permissions, auditability and contextual accuracy become critical requirements rather than back-office concerns.
Top Insights
- AgentWorkx embeds AI agents within Stibo Systems’ governed MDM environment instead of treating agent deployment as a separate enterprise software layer.
- Two initial agents target data onboarding and product content generation, combining automation with structured enterprise product information.
- The platform lets enterprises use prebuilt agents or develop custom agents while retaining existing roles, permissions and governance controls.
- Stibo Systems is positioning master data as foundational infrastructure for reliable enterprise AI and autonomous workflow execution.
- The broader market is shifting from AI experimentation toward governed deployment, measurable ROI and production-scale agentic workflows.
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