📊 Agent Evaluation Metrics
Measure and optimize AI agent performance with key metrics
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Retrieval-Augmented Generation
Introduction to Agent Evaluation
🎯 Why Evaluate Agents?
AI agents operate autonomously, making evaluation critical for reliability. Unlike traditional models, agents take actions, use tools, and make decisions. We need comprehensive metrics to measure success, efficiency, quality, and safety across diverse tasks.
💡
Key Insight
Good metrics help identify failures, optimize costs, and build trust in autonomous systems.
✅
Success Tracking
Measure task completion rates and goal achievement
⚡
Efficiency
Monitor resource usage and execution speed
🎯
Quality
Assess output accuracy and decision quality
📋 Evaluation Categories
1
Task Performance
Success rate, accuracy, completion time
2
Resource Efficiency
Token usage, API calls, cost per task
3
Output Quality
Coherence, relevance, factual accuracy
4
Safety & Reliability
Error handling, constraint adherence
✅ Benefits
- •Identify weak points
- •Optimize costs
- •Build confidence
- •Compare approaches
⚠️ Challenges
- •Defining success
- •Ground truth data
- •Multi-turn evaluation
- •Subjective quality