When to use which reasoning pattern in Agentic AI - Model Metrics & Evaluation

Metrics & Evaluation - When to use which reasoning pattern

Which metric matters and WHY

Choosing the right reasoning pattern means picking the best way to solve a problem. Metrics help us see if the chosen pattern works well. For example, if the task is to classify images, accuracy and F1 score matter because we want correct and balanced results. If the task is to generate text, metrics like BLEU or ROUGE show how close the output is to human language. Understanding the goal helps pick the right metric and reasoning pattern.

Confusion matrix or equivalent visualization

Confusion Matrix Example for Classification Reasoning Pattern:

          Predicted
          Pos   Neg
Actual Pos  85    15
       Neg  10    90

- True Positives (TP): 85
- False Positives (FP): 10
- True Negatives (TN): 90
- False Negatives (FN): 15

This matrix helps calculate precision, recall, and F1 to evaluate reasoning quality.

Precision vs Recall tradeoff with examples

Different reasoning patterns balance precision and recall differently. For example:

• High precision needed: Spam filter should rarely mark good emails as spam. So, use a reasoning pattern that minimizes false positives.
• High recall needed: Cancer detection should find all cancer cases, even if some false alarms happen. So, use a reasoning pattern that minimizes false negatives.

Choosing reasoning depends on which error is costlier.

What "good" vs "bad" metric values look like

For reasoning patterns in classification:

• Good: Precision and recall both above 0.8, F1 score near 0.85 or higher.
• Bad: Precision or recall below 0.5, showing many wrong or missed results.

For generation tasks, good BLEU or ROUGE scores are closer to 1.0, bad scores near 0.

Common pitfalls in metrics

• Accuracy paradox: High accuracy can be misleading if data is unbalanced.
• Data leakage: Using future or test data in training inflates metrics falsely.
• Overfitting: Great training metrics but poor real-world results show reasoning pattern is too tailored to training data.

Self-check question

Your model uses a reasoning pattern and shows 98% accuracy but only 12% recall on fraud cases. Is it good for production? Why or why not?

Answer: No, because it misses most fraud cases (low recall). For fraud detection, catching fraud (high recall) is more important than overall accuracy.

Key Result

Choosing the right reasoning pattern depends on the task and metric tradeoffs like precision vs recall.

Practice

1. Which reasoning pattern is best when you want a clear, step-by-step explanation from an AI?

easy

A. Step-by-step reasoning

B. Direct reasoning

C. Probabilistic reasoning

D. Hybrid reasoning

Solution

1. Step 1: Understand the purpose of step-by-step reasoning

   Step-by-step reasoning breaks down problems into clear, ordered steps for easy understanding.

2. Step 2: Match the pattern to the task

   When you want clear explanations, step-by-step is the best fit because it shows each part of the process.

3. Final Answer:

   Step-by-step reasoning -> Option A

4. Quick Check:

   Clear explanation = Step-by-step reasoning [OK]

Hint: Choose step-by-step for clear, detailed explanations [OK]

Common Mistakes:

• Confusing direct reasoning with step-by-step
• Using probabilistic reasoning for simple tasks
• Thinking hybrid reasoning is always best

2. Which of the following is the correct syntax to describe direct reasoning in AI?

easy

A. AI solves problem by breaking into steps

B. AI guesses answer based on chance

C. AI gives answer immediately without steps

D. AI mixes step-by-step and guessing

Solution

1. Step 1: Understand direct reasoning meaning

   Direct reasoning means AI gives an answer immediately without showing steps.

2. Step 2: Match syntax to meaning

   AI gives answer immediately without steps correctly describes direct reasoning as giving an answer immediately without steps.

3. Final Answer:

   AI gives answer immediately without steps -> Option C

4. Quick Check:

   Direct reasoning = immediate answer [OK]

Hint: Direct reasoning means no steps, just answer [OK]

Common Mistakes:

• Mixing step-by-step with direct reasoning
• Thinking direct reasoning involves guessing
• Confusing hybrid reasoning with direct

3. Given this code snippet simulating reasoning patterns, what will be the output?

def reasoning(pattern):
    if pattern == 'direct':
        return 'Answer immediately'
    elif pattern == 'step':
        return 'Explain step 1, then step 2'
    elif pattern == 'probabilistic':
        return 'Guess with chance'
    else:
        return 'Unknown pattern'

print(reasoning('step'))

medium

A. Answer immediately

B. Explain step 1, then step 2

C. Guess with chance

D. Unknown pattern

Solution

1. Step 1: Check the input to the function

   The function is called with 'step' as the pattern argument.

2. Step 2: Follow the if-elif conditions

   When pattern is 'step', the function returns 'Explain step 1, then step 2'.

3. Final Answer:

   Explain step 1, then step 2 -> Option B

4. Quick Check:

   Input 'step' returns explanation steps [OK]

Hint: Match input string to if-elif return value [OK]

Common Mistakes:

• Choosing output for 'direct' instead of 'step'
• Ignoring else case
• Misreading the function logic

4. This code is meant to select a reasoning pattern based on problem complexity. What is the bug?

def select_pattern(complexity):
    if complexity > 5:
        return 'step-by-step'
    elif complexity > 10:
        return 'probabilistic'
    else:
        return 'direct'

print(select_pattern(12))

medium

A. Print statement syntax is incorrect

B. Missing return statement in else block

C. Function does not handle complexity less than 0

D. The order of conditions is wrong; higher complexity checked second

Solution

1. Step 1: Analyze the if-elif conditions order

   The first condition checks if complexity > 5, which is true for 12, so it returns immediately.

2. Step 2: Identify the logic error

   The second condition (complexity > 10) is never reached because the first condition is broader and comes first.

3. Final Answer:

   The order of conditions is wrong; higher complexity checked second -> Option D

4. Quick Check:

   Check condition order for correct logic [OK]

Hint: Check if conditions from most specific to general [OK]

Common Mistakes:

• Ignoring condition order importance
• Assuming else block missing return causes error
• Thinking print syntax is wrong

5. You have a complex problem with uncertain data and need the AI to both guess and explain some steps. Which reasoning pattern should you choose?

hard

A. Hybrid reasoning

B. Step-by-step reasoning

C. Direct reasoning

D. Probabilistic reasoning

Solution

1. Step 1: Understand problem needs

   The problem is complex with uncertain data and requires both guessing and explanation.

2. Step 2: Match reasoning pattern to needs

   Hybrid reasoning combines step-by-step explanation and probabilistic guessing, fitting the problem best.

3. Final Answer:

   Hybrid reasoning -> Option A

4. Quick Check:

   Complex + uncertain + explanation = Hybrid reasoning [OK]

Hint: Use hybrid for complex, uncertain, and explanatory tasks [OK]

Common Mistakes:

• Choosing only probabilistic reasoning for explanation
• Picking direct reasoning for complex problems
• Ignoring hybrid as a combined approach