Regression testing for agent changes in Agentic AI - Model Metrics & Evaluation

Metrics & Evaluation - Regression testing for agent changes

Which metric matters for regression testing and WHY

When we update an AI agent, we want to make sure it still works well. For regression testing, we focus on error metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), or Root Mean Squared Error (RMSE). These numbers tell us how far the agent's predictions are from the true answers.

We compare these errors before and after changes. If errors get bigger, the new agent might have problems. So, these metrics help us catch mistakes introduced by updates.

Confusion matrix or equivalent visualization

Regression tasks don't use confusion matrices because they predict numbers, not categories. Instead, we look at error values.

Example errors before and after agent update:

| Metric | Before Update | After Update |
|--------|---------------|--------------|
| MAE    | 2.5           | 3.8          |
| MSE    | 9.0           | 15.0         |
| RMSE   | 3.0           | 3.87         |

Higher errors after update mean the agent's predictions got worse.
    

Tradeoff: Stability vs Improvement

When changing an agent, we want it to improve but also stay stable. If the agent's error gets smaller, that's good. But if it gets bigger, it means the update caused problems.

Sometimes, a small increase in error is okay if the agent gains new skills. But big error jumps mean we should fix the update.

Think of it like fixing a car: you want it to run better, not worse after repairs.

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

Good: After agent changes, error metrics stay the same or get smaller. For example, MAE stays around 2.5 or drops to 2.0.

Bad: Errors increase a lot, like MAE jumping from 2.5 to 5.0. This means the agent's predictions are less accurate.

Good regression testing means catching these bad changes before releasing the agent.

Common pitfalls in regression testing metrics

• Ignoring small error changes: Sometimes small error increases are normal, but ignoring big jumps is risky.
• Testing on different data: Comparing errors on different test sets can mislead results.
• Overfitting to test data: If the agent is tuned too much on test data, errors look good but real performance drops.
• Not tracking multiple metrics: Using only one error metric can hide problems. Check MAE, MSE, and RMSE together.

Self-check question

Your agent update shows 98% accuracy but the Mean Absolute Error increased from 2.0 to 6.0. Is this good?

Answer: No. Accuracy is not a good metric for regression. The big increase in MAE means predictions are less accurate. The update likely caused problems and needs review.

Key Result

For regression testing agent changes, tracking error metrics like MAE and MSE before and after updates is key to detect performance drops.