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.