Introduction

Semantic Kernel is a powerful SDK designed to integrate AI models into applications, enhancing responsiveness and contextual understanding. It provides essential tools for chat completion, multi-agent coordination, and data retrieval, empowering developers to build sophisticated AI solutions.

The Virtual Customer Success Manager (VCSM), introduced at Hackathon ’24, is a C# .NET project built using Semantic Kernel. VCSM revolutionizes customer support, onboarding, and engagement by delivering personalized interactions, proactive services, and tailored recommendations. By leveraging Semantic Kernel, VCSM dynamically adapts to user needs, improving customer experience and streamlining business operations. Here is a quick demo of this solution. 

Video provider requires cookies to play the video. Accept to continue or watch it directly on the provider's site.

Accept

We also offer a value-based service entitled, Upskilling Agentic AI Systems, designed to upskill and help your teams capture AI-driven business value. This service accelerates your ability to build Azure OpenAI-powered Agentic AI systems, with customer success embedded in the delivery by design.

VCSM's Core Use Cases driven by SK Features

VCSM leverages three core features of Semantic Kernel—Chat Completion Model, Multi-Agent Framework, and Vector Store—to deliver its three key use cases: Personalized Onboarding, Proactive Support, and Tailored Recommendations.

1. Personalized Onboarding

Semantic Kernel’s Chat Completion Model and Plugins enable VCSM to deliver interactive, real-time onboarding experiences. Through Automatic Function Calling, the system dynamically guides users through product setup, invoking the appropriate functions based on user queries, such as GuideInitialSetup() or ExplainFeatures().

2. Proactive Support

The Multi-Agent Framework coordinates specialized agents to monitor and address system issues. A Master Agent oversees agents like the Teams Agent (monitoring Microsoft Teams) and Azure Agent (analyzing AKS health). Together, they proactively resolve issues, ensuring system stability.

3. Tailored Recommendations

With the Vector Store feature, VCSM retrieves customer-specific data—such as usage patterns and preferences—using the Retrieval Augmented Generation (RAG) pattern. This allows VCSM to deliver personalized tips and recommendations in real-time, providing users with highly relevant insights based on their past interactions and stored data.

By combining these Semantic Kernel features, VCSM delivers a seamless, user-focused experience that transforms how businesses approach customer engagement and support.

VCSM’s Code Breakdown

In this section, we will provide a high-level overview of the key modules that implements the onboarding, support, and recommendation functionalities of the system. You can find the full implementation and setup details on the GitHub repository.

1. VCSM_PersonalizedOnboarding.cs

This module focuses on implementing Personalized Onboarding using Semantic Kernel's Plugins. The TeamsOnboardingPlugin, assists with Microsoft Teams’ functionality, offering an interactive onboarding experience by automatically switching between the following functions based on user requests:

• GuideInitialSetup(): Assists users with the first-time setup of Microsoft Teams.
• ExplainFeatures(): Provides detailed explanations of key Microsoft Teams features, including chat, meetings, and file sharing.
• ProductivityTips(): Suggests best practices and tips for improving productivity when using Microsoft Teams.

This code dynamically adjusts to user queries, making the onboarding process tailored and efficient.

2. VCSM_ProactiveSupportWithAgents.cs

This module handles Proactive Support by implementing Semantic Kernel's Multi-Agent Framework. The Master Agent orchestrates the responses between two specialized agents:

• Teams Agent: Monitors the performance of Microsoft Teams, identifying issues such as frequent disconnections or crashes.
• Azure Agent: Analyzes Azure Kubernetes Service (AKS) for potential problems, such as CPU and memory overuse, or node exhaustion.

After deliberation between the Master Agent and the specialized agents, and based on an agreed-upon Termination Strategy, a crafted recommendation response is provided for the incoming system alert. This parallel coordination enables VCSM to offer proactive, real-time support, ensuring the system remains healthy and optimized.

3. VCSM_TailoredRecommendations.cs

This module is responsible for providing Tailored Recommendations by leveraging the Vector Store to offer personalized insights. The system uploads customer-specific content (e.g., PDFs like teamsguide.pdf or teamsrecommendations.pdf) and responds to user queries in a highly customized manner. The workflow operates as follows:

• Profile Setup Instructions: For queries like "How do I set up my profile?", the Vector Store retrieves relevant instructions and passes them to the Large Language Learning Model (LLM), which generates a personalized response.
• Feature Overview: When users ask, "What features does Teams offer?", the Vector Store pulls detailed information on Teams features, enabling the system to provide a comprehensive overview.
• Productivity Tips: The system extracts actionable insights from stored content to deliver personalized tips for improving productivity with Microsoft Teams.

By combining real-time user queries with data retrieved from the Vector Store, VCSM generates context-aware, highly relevant recommendations that enhance the user experience.

Conclusion

The Virtual Customer Success Manager (VCSM), built using Semantic Kernel and leveraging Azure OpenAI, demonstrates the power of AI-driven solutions in transforming customer interactions. By seamlessly integrating chat completion, multi-agent systems, and personalized data retrieval, VCSM provides dynamic, context-aware onboarding, proactive support, and tailored recommendations. This solution not only enhances user experience but also streamlines business operations, offering a scalable and extensible approach to customer success management. This project was also featured in the Semantic Kernel Blogs found in this link.