Master Data Management Built for the Agentic Enterprise

STEP is the trusted intelligence platform that gives AI agents, automated pipelines and human teams a single, governed source of master data to reason and act from. Enterprise AI is moving from assisted workflows, where a person reviews every output, to autonomous execution, where agents query data, reason over it and act on it directly. That shift changes what is required of master data: It can no longer function as a static system of record. It has to operate as live decision infrastructure that agents can query, trust and act on safely.

STEP is built for that requirement. As a multidomain master data management (MDM) platform, it resolves, governs and serves master data so that every decision in the enterprise, whether made by a person, a pipeline or an agent, starts from the same trusted record. It functions as the authoritative, queryable master data layer underneath both human-led and agent-led operations.

A unified multidomain foundation

STEP manages product, customer, supplier, location, asset and reference data on one platform, built on a shared metadata model, a shared governance framework and a shared integration layer. Instead of stitching together domain-specific point solutions, each with its own rules and integration pattern, organizations get one consistent set of governance controls applied uniformly across every domain.

Multi-tenant scalable architecture

STEP runs on Microsoft Azure using a containerized microservices architecture that scales elastically with workload demand. The multitenant model isolates each tenant logically while sharing compute and storage resources within a secured Azure environment, keeping onboarding fast and total cost of ownership low. Infrastructure-as-code provisioning supports secure, repeatable deployments and integrates with the identity and authentication systems already in place.

Fault-tolerant service isolation and global availability zones keep latency low and uptime high, even as agentic workloads push more concurrent requests through the platform than traditional, human-paced usage ever generated. 

STEP delivers trustworthy intelligence to agents

STEP pulls master data in from across the enterprise, resolves and governs it into trusted master records, then serves that intelligence downstream to whichever system, person or agent needs it next.

Trustworthy records at query time

STEP gives agents more than a value; it gives them context. Configurable survivorship rules, enrichment logic and version control mean an agent querying STEP receives a record already resolved as canonical, not one of several competing versions. Alongside the value itself, agents can retrieve data quality scores, trust indicators and lineage metadata, grounding each decision in a record that is both correct and explainable after the fact.

Real-time accuracy for agentic workflows

Agentic workflows run in the present tense. Overnight batch updates that are adequate for a dashboard are not adequate for an agent deciding what to do right now. STEP validates and enriches records at ingestion and propagates updates immediately, so downstream systems and agents read current state instead of polling for it or working from a stale cache.

Governance that makes autonomy defensible

STEP enforces role-based access control and keeps an auditable history of every change. No record reaches the master store without passing the validation, approval and enrichment steps an organization defines, regardless of whether the trigger was a human steward or an automated agent. This control plane is what makes scaling automation defensible rather than risky. 

Interoperability from source to agent

Data flows in from everywhere, so STEP connects to ERP, CRM and adjacent systems through a library of more than 100 prebuilt connectors, bringing data in and resolving and serving it back out without locking an organization into a single vendor’s ecosystem. For agent access specifically, the Stibo Systems MCP Server exposes master data over the Model Context Protocol, letting agents query, retrieve and act on master records using the same protocol they already use across the rest of their tool ecosystem, without requiring bespoke integration for every new agent or tool. 

Semantic relationships for cross-domain reasoning

Agents reasoning across domains need to understand how entities relate to one another, not just retrieve isolated attribute values. STEP’s data model represents every entity as a node in a graph structure, with typed, directional relationships, configurable hierarchies and attribute inheritance built in natively. Governed relationship definitions, metadata and classification schemes carry semantic meaning that agents can interpret directly from structure, instead of inferring it statistically from unstructured text. 

AI-ready architecture on Microsoft Azure

STEP’s agentic workflows handle deduplication, anomaly detection and data enrichment automatically, keeping master data clean, contextual and ready to act on. These workflows scale through Azure Kubernetes Service (AKS), with Azure Event Grid and Azure Service Bus providing the real-time responsiveness agentic operations depend on. Integration with Microsoft Fabric keeps master data structured, governed and traceable, which also makes it suitable for AI model training and analytics, not just operational use. Dataflows apply business rules to cleanse and transform records, and unified access through OneLake, with permissions inherited directly from STEP, lets that high-quality data flow into Power BI, Synapse and Azure ML without rebuilding access controls at every hop.