Learn Microsoft AI guides you through orchestrating multiple AI agents in a Blazor app using the Semantic Kernel framework, sharing code examples and orchestration strategies tailored for .NET developers.

Orchestrating AI Agents in Blazor Using Microsoft Semantic Kernel

Author: Learn Microsoft AI

Overview

Discover how to implement concurrent orchestration of intelligent AI agents using the Semantic Kernel Agent Framework in a Blazor application. This guide provides a step-by-step walkthrough, from initial setup to a hands-on demo, detailing code structure and design patterns for scalable agent management.

Table of Contents

• Overview of Implementation Plan
• Adding Semantic Kernel to Dependency Injection (DI)
• Registering ChatCompletionAgents in DI
• Setting Up the Orchestration Service
• Creating the Agent Resolver
• Implementing the Transformer Handler
• Registering ProcessRuntime & Invoking Agents
• Injecting Orchestration into a Blazor Page
• Agent Orchestration Demo
• Page-Based Orchestration Explained
• Grouping and Injecting Agents Per Page
• Concurrent Orchestration Logic
• Optimization and Code Improvement

Semantic Kernel in Blazor

• Add Semantic Kernel to your Blazor app via Dependency Injection for centralized management.

Registering Agents

• Register specialized agents (e.g., Movies Agent, Food Agent) as services.
• Use ChatCompletionAgents for AI-driven dialogue and recommendations.

Orchestration Service

• Set up a ConcurrentOrchestrationService to coordinate multiple agents in parallel.
• Demonstrate parallel execution and concurrent orchestration for increased responsiveness.

Dependency Injection & Agent Invocation

• Inject orchestration service into desired Blazor pages for dynamic agent management.
• Use an Agent Resolver for runtime selection of relevant agents based on user actions or page context.

Demo Highlights

• Page-based orchestration allows agents to be grouped and invoked based on UI context.
• ProcessRuntime enables seamless and efficient triggering of multiple agents.
• Example: running Movies Agent and Food Agent in the same workflow.

Use Cases

• Smart chatbots with distinct knowledge domains
• Recommendation systems leveraging multiple specialized agents
• Interactive, responsive AI features in .NET web frontends

Optimization Areas

• Code organization for scalability
• Enhancing agent resolution strategies
• Improving parallelism and error handling

Takeaways

• Strong fit for .NET and Blazor developers aiming to build multi-agent, intelligent applications.
• Demonstrates practical orchestration patterns with Microsoft's Semantic Kernel.

Links:

• Learn Microsoft AI YouTube
• LinkedIn profile
• GitHub repository