Decision-Making and Reasoning

Decision-Making and Reasoning

How agents think through problems and choose actions.

Chain-of-Thought Reasoning

Breaking complex problems into sequential reasoning steps:

Question: "Should I recommend Product A or B for this customer?"

Thought 1: Customer needs high performance (mentioned in requirements)
Thought 2: Product A has better benchmarks but costs 2x more
Thought 3: Customer's budget is limited (mentioned earlier)
Thought 4: Product B offers best value within their budget
Conclusion: Recommend Product B with explanation of tradeoffs

Tree-of-Thought Reasoning

Exploring multiple reasoning paths simultaneously:

• Generate multiple possible approaches
• Evaluate each path's likelihood of success
• Select the most promising path
• Backtrack if a path fails

Confidence Scoring

Agents should know when they're uncertain:

def decide_with_confidence(query, context):
    response = llm.generate(query, context)
    confidence = evaluate_confidence(response, context)
    
    if confidence > 0.8:
        return response
    elif confidence > 0.5:
        return f"I'm not fully certain, but: {response}"
    else:
        return "I'm not confident enough to answer. Let me escalate this."

Human-in-the-Loop Patterns

When to involve humans:

• Approval gates: Agent proposes action, human approves
• Escalation: Agent recognizes it can't handle the task
• Feedback loops: Human corrects agent, agent learns
• Oversight: Human monitors agent actions in real-time

Key Takeaways

• Chain-of-thought improves reasoning on complex problems
• Tree-of-thought explores multiple solutions in parallel
• Confidence scoring prevents agents from acting on uncertain information
• Human-in-the-loop ensures safety for high-stakes decisions