Guest Blog: Building Multi-Agent Systems with Multi-Models in Semantic Kernel – Part 1

Why Multi-Agent Systems?

• A single agent can effectively tackle specific tasks.
• Complex problems often demand collaboration.

Why Multi-Model Systems?

• Multi-model systems introduce modularity and adaptability to AI systems.
• Different agents handle various tasks, such as customer interactions, visual data interpretation, and real-time translations.

Building Multi-Agent Systems

• Semantic Kernel provides an Agent Framework for building agents.
• Core components like classes and methods facilitate agent interactions and collaboration.
• A getting started notebook for Semantic Kernel is available to learn about the Agent Framework's capabilities.
• The use of Azure OpenAI, Google Gemini, and Meta's Llama leverages multi-model capabilities for better reasoning and outputs.

Exploring the Process

• The User submits a request for a travel plan.
• The Agent Group Chat directs the task to the Travel Planner Agent.
• The Travel Planner Agent creates an itinerary and calculates the budget.

Conclusion

• There are various functionalities within the Agent Framework that developers can explore.
• Stay tuned for part 2 on human in the loop interactions.

Kudos to Arafat from the Semantic Kernel team for sharing this insightful article. We look forward to part 2!