Regression testing for agent changes in Agentic AI - Model Pipeline Trace

Model Pipeline - Regression testing for agent changes

This pipeline tests if changes to an AI agent affect its performance. It runs the agent on past tasks, compares new results to old ones, and checks for any unexpected drops in accuracy or increases in errors.

Data Flow - 5 Stages

1Load historical test data

1000 tasks x 10 features→Load previously saved test tasks and expected outputs→1000 tasks x 10 features

Task: Classify email as spam or not; Features: word counts, sender info

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2Preprocess input data

1000 tasks x 10 features→Normalize features and encode categorical data→1000 tasks x 10 normalized features

Word counts scaled between 0 and 1; sender encoded as number

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3Run agent on test data

1000 tasks x 10 normalized features→Agent makes predictions using updated model→1000 predictions

Predicted spam probability for each email

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4Compare predictions to baseline

1000 predictions and 1000 baseline predictions→Calculate difference in accuracy and error rates→Summary metrics: accuracy drop, error increase

Accuracy dropped from 95% to 93%, error increased by 2%

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5Report regression results

Summary metrics→Generate report highlighting any performance drops→Report document

Report shows 2% accuracy drop, flags possible regression

Training Trace - Epoch by Epoch

Loss
0.5 |****
0.4 |****
0.3 |****
0.2 |***
0.1 |
    +----------------
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.45	0.78	Initial training with new agent version shows moderate loss and accuracy
2	0.38	0.82	Loss decreased and accuracy improved, agent learning better
3	0.32	0.86	Continued improvement, training progressing well
4	0.28	0.89	Loss decreasing steadily, accuracy nearing target
5	0.25	0.91	Training converging, agent performing well on training data

Prediction Trace - 4 Layers

Layer 1: Input preprocessing

Layer 2: Agent prediction

Layer 3: Thresholding

Layer 4: Compare to baseline

Model Quiz - 3 Questions

What does the 'Compare predictions to baseline' stage check for?

A If the input data is correctly normalized

B If the training loss is decreasing

C If the new agent's predictions are worse than before

D If the agent's code has syntax errors

Key Insight

Regression testing helps catch unintended drops in agent performance after changes. By comparing new predictions to past results, we ensure the agent stays reliable and accurate.