I am currently looking into how the Semantic Kernel can be used to manage complex, multi-step workflows. Specifically, I want to know how it handles task delegation between different AI agents in a .NET environment. Does it require a custom orchestration layer, or can the built-in planners handle autonomous handoffs effectively?
3 answers
The Semantic Kernel SDK excels here by using "Planners" to decompose goals into actionable steps. In a C# environment, you typically utilize the FunctionCallingStepwisePlanner which allows the kernel to dynamically select and execute plugins. For true multi-agent systems, you can define multiple kernels—each with a specific "Persona" and set of plugins—and then use a primary orchestrator kernel to delegate tasks. This modular approach ensures that each agent remains focused on its domain expertise, such as data retrieval or code generation, while the main kernel manages the state and sequence.
This sounds powerful, but how does the Semantic Kernel maintain context if one agent fails midway through a plan? Is there a built-in retry logic or a way to preserve the chat history across different agent handoffs without manual intervention?
I've found that using the KernelFunction decorators helps the planner understand exactly when to call a specific agent, making the orchestration much more predictable in production.
Melissa is right; well-defined metadata in your plugins is the "secret sauce" for Semantic Kernel success. It significantly reduces the hallucination rate during the planning phase.
Gregory, you can handle this by implementing a persistent ChatHistory object that is shared or passed between the agents. While the SDK has some basic retry policies via the underlying HttpClient, for complex agentic failures, most developers implement a "Plan Review" phase where a human or a 'supervisor agent' validates the output before proceeding to the next step in the Semantic Kernel workflow.