Topic 5: Reasoning Evaluation
What You’ll Learn
This topic teaches you how to:
- Evaluate chain-of-thought reasoning
- Test multi-step reasoning
- Assess planning quality
- Analyze reasoning traces
- Measure reasoning correctness
Why We Need This
Business Need
- Quality: Better reasoning = better results
- Trust: Users need to trust agent decisions
- Debugging: Understand why agents make decisions
Technical Need
- Reasoning quality: Measure how well agents reason
- Trace analysis: Understand agent thought process
- Planning evaluation: Test planning capabilities
Industry Use Cases
1. Decision-Making Agents
Company: Trading, healthcare, finance Use Case: Evaluate reasoning behind decisions
2. Problem-Solving Agents
Company: Research, engineering Use Case: Test multi-step problem solving
3. Planning Agents
Company: Automation, robotics Use Case: Evaluate planning quality
Industry-Standard Boilerplate Code
Reasoning Evaluator
"""
Reasoning Evaluator
Evaluates agent reasoning capabilities
"""
from typing import List, Dict
class ReasoningEvaluator:
"""Evaluate agent reasoning"""
def evaluate_chain_of_thought(self, agent, task: str) -> Dict:
"""Evaluate chain-of-thought reasoning"""
result = agent.run(task)
reasoning_steps = result.get('reasoning_steps', [])
return {
"steps_count": len(reasoning_steps),
"logical_flow": self._check_logical_flow(reasoning_steps),
"completeness": self._check_completeness(reasoning_steps, task)
}
def _check_logical_flow(self, steps: List) -> bool:
"""Check if reasoning steps flow logically"""
# Simplified: In production, use LLM or rule-based checks
return len(steps) > 0
def _check_completeness(self, steps: List, task: str) -> bool:
"""Check if reasoning addresses the task"""
# Simplified: In production, use semantic analysis
return True
Exercises
- Evaluate chain-of-thought
- Test multi-step reasoning
- Analyze reasoning traces
- Measure planning quality
Next Steps
- Topic 6: Safety evaluation
- Topic 7: Multi-agent evaluation