Architecting Enterprise AI Research Agents with Semantic Kernel

In this post, I'll share my experience and insights from architecting an enterprise-ready AI research agent using Microsoft's Semantic Kernel framework. This project involved porting an open-source AI research solution into a secure, scalable architecture suitable for enterprise deployment.

Background

GPTResearcher is a powerful open-source tool that leverages large language models to conduct comprehensive research on any topic. While impressive in capability, the original implementation lacked the enterprise-grade features necessary for corporate environments, including:

• Integration with enterprise identity systems
• Secure API management
• Real-time communication mechanisms
• Scalable architecture
• Deployment flexibility

The Semantic Kernel Approach

Microsoft's Semantic Kernel provided an ideal foundation for transforming this research agent into an enterprise solution. Semantic Kernel offers:

• A lightweight, extensible framework for AI orchestration
• Modular architecture with plugins
• Memory and context management
• Strong .NET integration

Architecture Overview

The enterprise solution architecture includes several key components:

1. Core Research Engine: Ported to Semantic Kernel plugins and skills
2. Authentication Layer: Azure AD integration with custom token validation
3. API Management: Azure APIM for rate limiting and security
4. Communication Layer: WebSocket and SSE implementation for real-time updates
5. Deployment Framework: Containerized for flexibility across environments

Implementation Highlights

Semantic Kernel Plugin Architecture

The research workflow was decomposed into Semantic Kernel plugins, each handling specific aspects of the research process:

public class ResearchPlugin
{
    [SKFunction]
    public async Task<string> GenerateResearchPlan(string topic)
    {
        // Implementation
    }

    [SKFunction]
    public async Task<string> ExecuteSearch(string query)
    {
        // Implementation
    }

    [SKFunction]
    public async Task<string> AnalyzeContent(string content)
    {
        // Implementation
    }
}

Real-time Progress Updates

One of the challenges was providing real-time progress updates during lengthy research operations:

public class ResearchStreamingService
{
    private readonly ISubject<ResearchUpdate> _updates = new Subject<ResearchUpdate>();
    
    public IObservable<ResearchUpdate> Updates => _updates.AsObservable();
    
    public void PublishUpdate(ResearchUpdate update)
    {
        _updates.OnNext(update);
    }
}

Azure AD Authentication

Custom authentication middleware was implemented to validate Azure AD tokens and enforce role-based access:

app.UseAuthentication();
app.UseAuthorization();

app.MapHub<ResearchHub>("/research-hub").RequireAuthorization(policy => 
{
    policy.RequireAuthenticatedUser();
    policy.RequireRole("ResearchUser");
});

Lessons Learned

Throughout this project, several key insights emerged:

1. Plugin Granularity: Finding the right balance of plugin granularity is crucial for maintaining flexibility without creating excessive complexity.

2. Memory Management: LLM context management requires careful design, especially when handling large research documents.

3. Authentication Flow: Enterprise authentication adds complexity that must be handled throughout the application.

4. Streaming Responses: Real-time research updates require thoughtful architecture patterns that work across different deployment models.

5. API Management Integration: Properly integrating with APIM involves more than simple routing - it requires thoughtful design of rate limiting, caching, and security policies.

Conclusion

Semantic Kernel provided an excellent foundation for transforming a standalone AI research tool into an enterprise-grade solution. The modular architecture, combined with Azure's identity and API management capabilities, created a secure, scalable solution for AI-powered research in corporate environments.

In future posts, I'll dive deeper into specific aspects of this architecture and share code examples for implementing enterprise patterns with Semantic Kernel.

Have you worked with Semantic Kernel or built enterprise AI agents? Share your experiences in the comments below.