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Agentic AI: Top 10 Metrics to Evaluate Multi Agent Systems
Top 10 Metrics to Test Agentic AI or Multi Agent Systems
Agentic AI Top 10 Evaluation Metrics.

1. Task Success Rate - Tracks the percentage of tasks successfully executed by AI agents.
2. Error Recovery Rate - The Error Recovery Rate , measures how effectively, an AI agent can identify and fix, its own mistakes, without external guidance.
3. Average Response Time - Average response time, reflects, the efficiency and latency of the agent, important for evaluating, real-time usability.

4. Conflict Resolution Rate - Conflict Resolution Rate (CRR), measures how effectively, an agentic AI system, can detect and resolve conflicting goals or actions on its own.

5. Collaboration Efficiency - Collaboration efficiency, evaluates how effectively, multiple agents, share information, coordinate, and complete tasks together.
6. Autonomy Score - Autonomy Score, Measures how independently, an agent can make decisions, without human intervention, or external guidance.
7. Resource Utilization Efficiency - Resource Utilization Efficiency, measures how effectively, an agent uses computational and memory resources, while completing its tasks.

8. Adaptability Index - Adaptability Index, assesses how effectively, an agent adjusts to, changes in environment, data, or task conditions.
9. Knowledge Retention & Utilization - Knowledge Retention & Utilization metric, measures how effectively, an agent recalls and applies, prior knowledge to new tasks.
10. Explainability & Interpretability - Explainability & interpretability score, measures how transparent and understandable, the agent’s reasoning and decisions, are to humans.


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