Enterprise Master Data Management for AI You Can Trust

Agents act on what they retrieve. STEP provides configurable rules, enrichment and version control, so agents can rely on governed master records at query time. No more guessing whether a record is canonical. 

At query time, agents can also check data quality scores, trust indicators and lineage metadata before acting, so decisions are grounded in records that are both canonical and explainable.

Agentic workflows operate in the present tense. And so should your data. Overnight batch pipelines work for dashboards, but they don’t work for agents making autonomous decisions at runtime. STEP validates and enriches records at ingestion and propagates updates. That way, downstream systems and agents aren’t dependent on polling or stale reads. 

When an agent can make autonomous decisions, it must be traceable, reversible and accountable.  

STEP enforces role-based access control and captures an auditable history of changes. No record reaches the master store without passing defined validation, approval and enrichment steps, whether the trigger is a human steward or an automated agent. 

 This is the control plane that makes agentic automation explainable and defensible.  

Data flows in from anywhere. Intelligence flows out to everything. STEP connects to ERP systems, CRM platforms and more through a library of more than 100 prebuilt connectors. STEP brings your data in from wherever it lives and resolves and governs it into trusted master records, then serves it downstream. All while remaining platform-agnostic. 

Agents don’t need custom integrations to reach master data. Stibo Systems MCP Server lets AI agents query, retrieve and act on your master data using the same protocol they use across the rest of their tool ecosystem. 

Agents need to understand relationships, not just retrieve values. STEP’s support for a semantic data graph isn't an add-on — it's enabled by the platform’s foundational data model capabilities. Every entity exists as nodes in graph structure with typed, directional relationships, configurable hierarchies and attribute inheritance.  

Semantic meaning is captured through governed relationship definitions, metadata, and ontologies or classification schemes that can be applied across any domain. This lets agents and AI systems interpret meaning from explicit structure instead of inference.  

Production agentic estates hit MDM infrastructure hard. More agents means more concurrent requests, tighter freshness requirements, and higher data volumes. STEP runs on Microsoft Azure with a multi-tenant, cloud-native architecture that scales with that demand. And because STEP manages master data across the full enterprise rather than a single domain, agents always work from a single, consistent record — not conflicting versions from siloed systems.