Branching and conditional logic in Agentic AI - Model Metrics & Evaluation

Metrics & Evaluation - Branching and conditional logic

Which metric matters for Branching and conditional logic and WHY

Branching and conditional logic in AI often control decision paths. To evaluate these decisions, accuracy is a key metric because it shows how often the logic leads to correct outcomes. However, if decisions split into categories with uneven importance, precision and recall become important to measure how well the logic handles specific cases.

Confusion matrix example

      Predicted
      |  Yes  |  No  |
    -------------------
    Yes |  40  |  10  |
    No  |  5   |  45  |

    TP = 40, FP = 10, FN = 5, TN = 45
    Total samples = 100
    

This matrix shows how many times the branching logic predicted "Yes" or "No" correctly or incorrectly.

Precision vs Recall tradeoff with examples

If the logic is used to approve loans, precision matters to avoid approving bad loans (false positives). High precision means most approved loans are good.

If the logic is used to detect fraud, recall matters to catch as many fraud cases as possible. High recall means few fraud cases are missed.

Good vs Bad metric values for branching logic

Good: Accuracy above 90%, precision and recall balanced above 85%, showing reliable decisions.

Bad: Accuracy high but recall very low (e.g., 20%), meaning many important cases are missed.

Common pitfalls in metrics

• Accuracy paradox: High accuracy can be misleading if classes are imbalanced.
• Data leakage: If future info leaks into logic, metrics look better but model fails in real use.
• Overfitting: Logic fits training data perfectly but performs poorly on new data.

Self-check question

Your branching logic model has 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). High accuracy is misleading if fraud is rare but important to catch.

Key Result

For branching and conditional logic, balance precision and recall to ensure decisions are both accurate and meaningful.