Microsoft Semantic Kernel
Master Semantic Kernel for building enterprise-grade AI agents with plugin architecture
Your Progress
0 / 5 completedKey Takeaways: Semantic Kernel Mastery
Check off each concept as you understand it. You've learned about Microsoft's enterprise SDK for AI agents with native plugin architecture, semantic memory, and automatic planning!
Progress: 0 / 15
Semantic Kernel is Microsoft's open-source SDK for building enterprise AI agents in .NET, Python, and Java
Plugin architecture lets you mix AI functions (prompts) and native code functions seamlessly
Two function types: Semantic (AI prompts with templates) and Native (decorated code methods)
[KernelFunction] and [Description] attributes make methods discoverable to SK and planners
Built-in semantic memory with embeddings enables RAG patterns and knowledge retrieval
Memory integrates with Azure Cognitive Search, Pinecone, Qdrant, and other vector databases
Function Calling Stepwise Planner automatically decomposes goals into executable function sequences
Planners use function descriptions to decide which functions to call and in what order
Native integration makes SK feel like natural .NET/Python code, not an external API
Enterprise features: Managed Identity, Key Vault, Application Insights, retry policies
Use Azure Managed Identity to eliminate API keys and secrets from code
Built-in logging tracks function calls, token usage, planner decisions, and costs
Plugin best practices: single responsibility, descriptive names, mix AI and native functions
Perfect for existing .NET/Azure shops wanting to add AI capabilities to applications
SK is designed for production workloads with enterprise SLAs and Microsoft support