Develop AI agents with Semantic Kernel - Jakob Ehn - NDC Oslo 2024

Semantic Kernel is an open-source framework from Microsoft that helps combine language models with application code through plugins, planners, and personas

Plugins can be either prompts (defined in YAML) or native code (C#) and serve as the bridge between AI and data/services

Models are stateless and require context/history to be passed with each interaction - Semantic Kernel handles this automatically

Function calling allows models to identify and call appropriate plugins based on user queries without explicit instructions

Different planners are available:

• Sequential planner for basic function chaining
• Handlebars planner for more structured planning
• Custom planners can be implemented for specific needs

Temperature settings control how deterministic vs creative the model responses are

Co-pilots built with Semantic Kernel can:

• Access internal company data and services
• Maintain conversation context
• Execute actions through plugins
• Validate before executing sensitive operations

Best practices:

• Use simpler/cheaper models when possible
• Store prompts separately from code
• Include examples in prompts for better results
• Break down complex tasks into smaller specialized agents

Multiple models can be used in the same application for different purposes (GPT-3.5 vs GPT-4)

Support exists for .NET, Python and Java with similar feature parity across platforms