Microsoft Semantic Kernel: Production Architecture for AI Agents

Microsoft Semantic Kernel: Production Architecture for AI Agents

A comprehensive technical deep dive into Microsoft's Semantic Kernel—the AI orchestration SDK designed for production enterprise systems. Learn the core abstractions (Kernel, Plugins, Planners, Memory), integration patterns with Azure OpenAI, and production deployment strategies for building robust AI applications that scale.

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What you will learn:
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Semantic Kernel Fundamentals
What Semantic Kernel is and why Microsoft built it
Core philosophy: orchestration over implementation
Semantic Kernel vs LangChain: architectural differences and trade-offs
When to choose Semantic Kernel for enterprise AI systems

The Kernel: Central Orchestration
Kernel architecture and lifecycle management
Service registration and dependency injection patterns
Configuring AI services (Azure OpenAI, OpenAI, local models)
Builder pattern for flexible kernel construction

Plugins: Extending LLM Capabilities
Native functions vs semantic functions
Plugin architecture and design patterns
Creating custom plugins for enterprise integrations
Function calling and automatic function selection

Planners: Autonomous Task Orchestration
Planner types: Sequential, Stepwise, Handlebars
When to use planners vs explicit orchestration
Planner limitations and failure modes
Production considerations: cost, latency, reliability

Memory & Context Management
Semantic memory architecture
Vector store integrations (Azure AI Search, Qdrant, Pinecone)
Conversation history and context window management
RAG patterns with Semantic Kernel

Connectors & Integrations
Azure OpenAI connector configuration
Multi-model routing strategies
Integration with Microsoft Graph and enterprise services
Building custom connectors

Production Deployment Patterns
Error handling and retry strategies
Observability with OpenTelemetry
Token management and cost optimization
Security considerations for enterprise deployment

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Timestamps:
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[00:00] Introduction: AI Agents and the Orchestration Challenge
[00:23] What is Semantic Kernel? Microsoft's AI Orchestration SDK
[01:38] Mental Model: Semantic Kernel as Middleware Layer
[02:27] Where Semantic Kernel Fits in Production Architecture
[03:03] AI Provider Integration (Azure OpenAI, OpenAI, Hugging Face, Local Models)
[03:28] The Kernel Object: Central Orchestrator
[04:13] Kernel Stateless Design and Why It Matters
[04:41] Service Registration Methods
[05:35] Configuration Best Practices (Secrets, Timeouts, Lifetime)
[06:31] Plugins and Functions: Core Building Blocks
[08:03] Semantic Functions vs Native Functions
[09:00] Function Descriptions and Parameter Metadata
[09:33] Function Choice Behavior (Auto, Required, None)
[10:38] The Function Calling Loop Explained
[11:38] Making Function Calling Reliable in Production
[12:31] Memory and Embeddings
[13:26] Choosing Embedding Models
[14:34] Vector Store Considerations (Azure AI Search, Pinecone, Chroma)
[15:27] Planners Deprecation and the Process Framework
[17:35] When to Use Function Calling vs Process Framework
[18:12] Agent Framework (Now Generally Available)
[19:45] Building Effective Agents: Instructions and Plugins
[20:03] Multi-Agent Orchestration Patterns (Sequential, Concurrent, Handoff)
[21:03] Production Patterns: Error Handling and Resilience
[22:19] Observability and Debugging with OpenTelemetry
[23:58] Cost Management and Token Optimization
[24:04] When to Use Semantic Kernel
[25:07] When to Consider Alternatives
[26:56] Summary and Key Takeaways

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About Me:
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I'm Mukul Raina, a Senior Software Engineer and Tech Lead at Microsoft, with a Master's in Computer Science from the University of Oxford. On this channel, I create technical deep dives on System Design and ML/AI architectures.

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