Customer Service Automation

 

 

The customer service automation system you're describing uses a sophisticated, multi-agent architecture built on specialized hardware and protocols. The Orchestrator Agent is the central brain that routes the customer request, leveraging a Knowledge Graph Neural Network (KGNN) for deep contextual analysis via the Model Context Protocol (MCP).

Here is a box diagram illustrating this workflow:

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Customer Service Automation Architecture Diagram

The system is built on a foundation of IBM Power 10/11 processors, which provide the high-performance computing necessary for the complex Graph Neural Network (KGNN) operations.

Layer	Component	Description
I/O	Customer Request	The initial input (e.g., an email, chat message, or voice query) from the customer.
---	$\downarrow$
Orchestration	Orchestrator Agent (Central Coordinator)	The core decision-maker that receives the request. Its primary task is Triage & Delegation.
---	$\downarrow$ (Queries for Context)
Context	Knowledge Graph (KG) & Reasoning	This layer provides real-time, structured context:
	PowerGraph KGNN Engine	The underlying Graph Neural Network that performs complex graph analytics on the enterprise data (customer history, product details, policies, etc.) to accurately classify the issue (e.g., "Billing" vs. "Technical").
	Model Context Protocol (MCP)	The standardized interface used by the Orchestrator to securely and efficiently query the Knowledge Graph and receive a structured context response.
---	$\downarrow$ (Decision: Route to Agent)
Specialized Agents	Billing Agent	If the issue is classified as "Billing": This agent handles payment analysis, invoice generation, refund calculations, and account updates.
	Technical Support Agent	If the issue is classified as "Technical": This agent handles troubleshooting, product information retrieval, and system diagnostics.
---	$\downarrow$
Output	Resolution/Response	A personalized, accurate resolution delivered back to the customer.

Underlying Infrastructure

The entire platform is underpinned by the IBM Power 10/11 architecture, which is key for accelerating the demanding workload of the PowerGraph KGNN due to its advanced memory and AI acceleration capabilities.

• Model Context Protocol (MCP): Acts as the universal adapter for the AI agents, allowing the Orchestrator to integrate the high-value, connected context from the Knowledge Graph without custom, brittle connectors. This ensures the decision-making is grounded in up-to-date, structured data.

• PowerGraph KGNN: While originally a power grid dataset benchmark, its application here signifies the use of a sophisticated Graph Neural Network to reason over a corporate Knowledge Graph. This is crucial for distinguishing complex issues, for instance, a "billing issue" that is actually a symptom of a "technical account error."