Error handling in tool calls in Agentic AI - Model Metrics & Evaluation

Metrics & Evaluation - Error handling in tool calls

Which metric matters for this concept and WHY

When handling errors in tool calls, the key metric is robustness. This means how well the system continues to work correctly even when some tools fail or give wrong results. We also look at error rate (how often errors happen) and recovery rate (how often the system fixes or handles errors successfully). These metrics matter because they show if the AI can keep helping users without crashing or giving wrong answers.

Confusion matrix or equivalent visualization (ASCII)

    Tool Call Outcome Confusion Matrix:

                 | Tool Success | Tool Failure |
    -------------|--------------|--------------|
    Handled Well |      TN      |      TP      |
    Not Handled  |      FP      |      FN      |

    Explanation:
    - TP: Tool failed but system handled it correctly (good error handling).
    - FP: Tool worked but system handled it as an error (false alarm).
    - FN: Tool failed but system failed to handle (bad error handling).
    - TN: Tool worked and system did not handle it (correctly proceeded).
    

Precision vs Recall tradeoff with concrete examples

Precision here means: When the system says it handled an error, how often was it correct?

Recall means: Out of all actual tool failures, how many did the system handle?

Example: If the system tries to fix every tool failure (high recall) but sometimes thinks there is an error when there is none (low precision), it may waste time fixing non-errors.

On the other hand, if it only fixes errors it is very sure about (high precision) but misses many real errors (low recall), users may see failures.

Good error handling balances precision and recall to fix most real errors without false alarms.

What "good" vs "bad" metric values look like for this use case

• Good: Precision and recall both above 90%. The system catches most errors and rarely raises false alarms.
• Bad: Precision below 50% means many false error fixes, confusing users. Recall below 50% means many errors go unhandled, causing failures.
• Error rate: Should be low, but some errors are normal. The key is how well the system recovers.
• Recovery rate: High recovery rate (above 85%) means the system fixes most errors it detects.

Metrics pitfalls

• Ignoring error types: Not all errors are equal. Some cause big failures, others minor delays. Metrics should reflect impact.
• Overfitting to test errors: If the system only learns to handle known errors, it may fail on new ones.
• Data leakage: Testing error handling on data the system already saw can give false high scores.
• Accuracy paradox: High overall accuracy can hide poor error handling if errors are rare.

Self-check question

Your system has 98% accuracy but only 12% recall on tool failures. Is it good for production? Why not?

Answer: No, it is not good. The high accuracy is misleading because tool failures are rare. The low recall means the system misses 88% of real errors, so many failures go unhandled, hurting user experience.

Key Result

Robust error handling balances high precision and recall to catch and fix most tool failures without false alarms.