Topic 7: Multi-Agent Evaluation

What You’ll Learn

This topic teaches you how to:

• Evaluate agent communication
• Test coordination between agents
• Measure collaborative performance
• Test competitive scenarios
• Evaluate multi-agent systems

Why We Need This

Business Need

• Complex systems: Many systems use multiple agents
• Coordination: Agents must work together
• Efficiency: Multi-agent systems should be efficient

Technical Need

• Communication: Test agent-to-agent communication
• Coordination: Evaluate coordination mechanisms
• Scalability: Test with multiple agents

Industry Use Cases

1. Multi-Agent Workflows

Company: Automation platforms Use Case: Evaluate agent teams working together

2. Agent Marketplaces

Company: Agent platforms Use Case: Test agent interactions

3. Distributed Systems

Company: Large-scale systems Use Case: Evaluate distributed agent coordination

Industry-Standard Boilerplate Code

Multi-Agent Evaluator

"""
Multi-Agent Evaluator
Evaluates multi-agent systems
"""
from typing import List, Dict

class MultiAgentEvaluator:
    """Evaluate multi-agent systems"""
    
    def evaluate_coordination(self, agents: List, task: str) -> Dict:
        """Evaluate agent coordination"""
        results = [agent.run(task) for agent in agents]
        
        return {
            "coordination_score": self._calculate_coordination(results),
            "communication_count": sum(r.get('communications', 0) for r in results),
            "success": all(r.get('success', False) for r in results)
        }
    
    def _calculate_coordination(self, results: List[Dict]) -> float:
        """Calculate coordination score"""
        # Simplified: In production, use sophisticated metrics
        return 0.8

Exercises

1. Test agent communication
2. Evaluate coordination
3. Test collaborative tasks
4. Measure multi-agent performance

Next Steps

• Topic 8: Real-world testing
• Topic 9: Automated evaluation