I'm curious about the multi-agent capabilities. Does anyone have experience in how Microsoft Semantic Kernel powers enterprise AI agents that can talk to each other to solve data silos? We want to ensure that one agent handles SQL while another handles the report generation for our executives.
3 answers
Implementing a multi-agent framework with Semantic Kernel is quite robust. You can define separate "Kernel" instances or specific "Plugins" for each agent's role. For your SQL agent, you would register a plugin with native functions that have read-only access to your database. The report agent would have a plugin for document generation. The Kernel then uses its orchestration logic to hand off the data from the SQL agent to the report agent. This separation of concerns is a core part of how Microsoft Semantic Kernel powers enterprise AI while maintaining strict data governance.
This architecture sounds great, but how do you manage the shared context between these agents? Do they share the same memory store or is each one isolated?
The "Automatic Function Calling" feature is the real winner here. It allows the agents to select the right tool for the job without you hardcoding every single logic path.
Exactly, Christopher. It's that flexibility that makes it so useful for dynamic data environments where the user's questions might change day to day.
Jason, usually you would use a Volatile or Persistent Memory store that the Kernel manages. You can pass the "ChatHistory" object or a shared "Semantic Memory" reference between the agents. This allows the reporting agent to understand the context of the data retrieved by the SQL agent without re-running the initial query, which is a very sleek way to maintain state in a complex workflow.