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.

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