Add an explainable, governable AI operating layer — without ripping out D365, Salesforce or SAP.
Capture every online and in-store action, let scoped AI agents read it, and fire the next journey — on top of the systems you already run. Real-time, MCP-native, Azure-deployed, fully audited.
Book a DemoMarketers lose up to 36% of the week manually pulling data across platforms. The gap between having data and acting on it is where revenue leaks — and where the loop closes.
Five decoupled but coordinated layers, each with a distinct responsibility: Data Fabric (facts) → Semantic Layer (meaning) → Agent Layer (judgment) → Application Layer (tools) → Workflow Layer (governance).
One governable operating layer, stacked on the systems you already run.
Unified data foundation — act in real time
Three canonical outputs: Event Stream (continuous behavioral signal — not periodic batch), Golden Record (continuously evolving identity resolution — not one-time cleanup), Serving Views (stable data contracts — upper layers insulated from storage changes).
Kafka as event backbone. Flink for real-time computation (windowing, state transitions, event-time semantics). StarRocks for high-performance analytical access. A Connector Catalog for managed integration with POS, e-commerce, CRM, service, advertising and warehouse systems.
AI agent operating layer — bounded judgment
A semantic layer translates columns and tables into customers and value (entities, attributes, relationships, intent, segments, metrics). Scoped agent roles (Consultant, Data Analyst, Marketing Designer, Loyalty Advisor) analyze, compare, judge and recommend — none hold execution authority by default.
Agent judgment is role-bound and linked to shared semantics and metrics. Agents cannot bypass the workflow layer. Enterprise trust comes from bounded responsibility, not broad model capability.
AI Frontier — CLI, Skills & MCP
API-first, cloud-native. Every callable capability has explicit inputs, outputs, constraints, failure behavior and governance boundaries across four domains: identity & entitlement, economic incentive, content & reach, service & responsibility.
MCP (Model Context Protocol) native — tool boundaries, parameters and expected outcomes described in machine-readable format. CLI Skills make business capabilities composable and versionable. Connects into broader AI ecosystems (Microsoft Copilot, custom agents) as a governed execution node.
Layer on top — don't rip & replace
Fact ownership, execution ownership and audit ownership must stay clearly assigned. The existing warehouse remains the authoritative store; the loop adds event processing, identity resolution and a serving layer on top.
A minimal-copy integration pattern layers the loop above D365, Salesforce, SAP, POS and e-commerce — they keep owning transaction records and master data, while the loop adds unified access, semantics, AI judgment and governed orchestration.
Enterprise governance & compliance
Four action types with escalating governance: read-only analysis, recommendation, controlled writes, high-risk execution. A three-layer authorization pyramid — Entity (what object?) → Action (what operation?) → Scope (how far?) — evaluates every request.
Agents are constrained to workflow context: they see only what the workflow exposes, use only authorized tools, and generate only permitted content. Human and AI operators converge on the same governance logic, with full traceability of scenario, node, judgment, rule and outcome.
What actually changes at the architecture layer
Architecture FAQ
How is the loop different from a CDP?
A CDP consolidates data but doesn't act. The loop adds four layers above the data: business semantics (what it means), AI agents (what should happen), callable tools via MCP (how to do it), and governed workflow (under what controls). The data fabric is Layer 1 of 5.
Do we need to replace D365, Salesforce, SAP or our existing systems?
No. The loop is explicitly a layer above existing systems. Your CRM, ERP, POS and e-commerce keep owning transaction records and master data. The loop adds unified data access, business semantics, AI judgment and governed orchestration above them — fact, execution and audit ownership stay clearly assigned.
What is MCP and why does it matter?
MCP (Model Context Protocol) is how AI agents discover and use business capabilities safely. Instead of hardcoded API integrations, MCP describes tool boundaries, parameters, constraints and expected outcomes in a machine-readable format — reducing misuse, enabling governance, and letting SocialHub.AI connect into broader enterprise AI ecosystems as a governed execution node.
How does the authorization model work for AI agents?
Agents do not receive standing roles or generic permissions. They are constrained to specific workflow contexts — they only see what the workflow exposes, only use tools it authorizes, and only generate content it permits. A three-layer pyramid (Entity → Action → Scope) evaluates every execution request.