25. Conversational Multi-Agent (AutoGen-style)
Mini-Project: Conversational Multi-Agent Problem Solving
An architect, developer, and tester agent hold a group chat moderated by an LLM speaker selector, debating and building on each other's ideas to design a high-scale real-time notification system.
Description
Conversational Multi-Agent creates a group chat where multiple agents converse with each other in natural language to solve a problem. Each agent has a distinct role and can respond to other agents' messages. A group chat manager determines speaking order — it can be round-robin, random, or LLM-selected. This pattern, popularized by Microsoft's AutoGen framework, enables emergent problem-solving through natural conversation.
The key insight is that agents build on each other's contributions conversationally, mimicking how human teams brainstorm and solve problems through discussion.
When to Use
- Brainstorming and creative problem-solving
- Code review and collaborative development
- Multi-perspective analysis
- When natural conversation flow is preferred over rigid workflows
Benefits
| Benefit | Description |
|---|---|
| Natural Interaction | Agents converse like human teammates |
| Emergent Solutions | Ideas build organically through dialogue |
| Flexible | No predefined workflow — conversation flows naturally |
| Transparent | Full conversation log shows reasoning process |
Architecture Diagram
flowchart TD
A[Problem Statement] --> B[Group Chat Manager]
B --> C[Agent: Architect]
B --> D[Agent: Developer]
B --> E[Agent: Tester]
C -->|Message| B
D -->|Message| B
E -->|Message| B
B --> F{Resolved?}
F -->|No - Next Speaker| B
F -->|Yes| G[Solution]
style A fill:#4CAF50,color:#fff
style B fill:#FF5722,color:#fff
style C fill:#2196F3,color:#fff
style D fill:#9C27B0,color:#fff
style E fill:#00BCD4,color:#fff
style G fill:#4CAF50,color:#fff