Agent Orchestration: Strongly-Typed Inputs and Results in Semantic Kernel
Learn Microsoft AI presents a guide on leveraging strongly-typed input and output models within Semantic Kernel agent workflows, demonstrating techniques for building scalable, robust AI applications.
Agent Orchestration: Strongly-Typed Inputs and Results in Semantic Kernel
In this video, Learn Microsoft AI demonstrates how to build robust AI agent workflows using strongly-typed input and output models in Semantic Kernel. The session focuses on defining structured input classes and employing type-safe orchestration strategies to enhance the efficiency and maintainability of AI solutions.
Key Topics Covered
Strongly-Typed Input and Output Models:
- How to define structured input classes and pass complex data into orchestrations in a type-safe manner
- Methods for returning structured outputs using custom classes for meaningful result processing
Custom Data Transformation in Agent Orchestration:
- Where and how to apply
InputTransformandResultTransformfor custom handling of data - Improving modularity, scalability, and reliability in AI agent system design
- Where and how to apply
Benefits of This Approach
- Type safety in orchestration code for easier maintenance and reduced errors
- Modularization of AI agent components for better scalability
- Enhanced application robustness by processing structured, meaningful results rather than plain text
Resources
Enhance your AI application development workflow by integrating strong typing principles with Semantic Kernel agents.