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.

Practice

1. What is the main purpose of regression testing for agent changes?

easy

A. To check if new changes break old agent behavior

B. To improve the agent's speed

C. To add new features to the agent

D. To change the agent's user interface

Solution

1. Step 1: Understand regression testing goal

   Regression testing is done to ensure that recent changes do not break existing functionality.

2. Step 2: Match purpose with options

   To check if new changes break old agent behavior clearly states checking if new changes break old behavior, which matches the goal.

3. Final Answer:

   To check if new changes break old agent behavior -> Option A

4. Quick Check:

   Regression testing = check old behavior intact [OK]

Hint: Regression testing checks old features still work after changes [OK]

Common Mistakes:

• Thinking regression testing adds new features
• Confusing regression testing with performance testing
• Assuming regression testing changes UI

2. Which of the following is the correct way to define a test case for regression testing an agent in Python?

easy

A. def test_agent(): assert agent.run(input) == expected_output

B. test agent run input equals expected output

C. def test_agent: return agent.run(input) == expected_output

D. function test_agent() { return agent.run(input) == expected_output; }

Solution

1. Step 1: Identify correct Python function syntax

   Python functions start with 'def', have parentheses, and a colon.

2. Step 2: Check assertion usage

   def test_agent(): assert agent.run(input) == expected_output uses 'assert' correctly to compare output, matching Python test style.

3. Final Answer:

   def test_agent(): assert agent.run(input) == expected_output -> Option A

4. Quick Check:

   Python test function with assert = def test_agent(): assert agent.run(input) == expected_output [OK]

Hint: Python test functions start with def and use assert [OK]

Common Mistakes:

• Missing parentheses or colon in function definition
• Using non-Python syntax
• Not using assert for test checks

3. Given the code below, what will be the output of the regression test?

class Agent:
    def run(self, x):
        return x * 2

def test_agent():
    agent = Agent()
    result = agent.run(3)
    assert result == 6
    print('Test passed')

test_agent()

medium

A. SyntaxError

B. Test passed

C. AssertionError

D. No output

Solution

1. Step 1: Understand agent run method

   The method multiplies input by 2, so run(3) returns 6.

2. Step 2: Check assertion and print

   The assertion checks if result == 6, which is true, so no error occurs and 'Test passed' prints.

3. Final Answer:

   Test passed -> Option B

4. Quick Check:

   3 * 2 = 6, assertion true, prints message [OK]

Hint: Check method output matches assertion to predict test result [OK]

Common Mistakes:

• Assuming assertion fails without checking output
• Confusing syntax errors with logic errors
• Ignoring print statement after assertion

4. Identify the error in the following regression test code and select the fix:

def test_agent():
    agent = Agent()
    result = agent.run(5)
    if result = 10:
        print('Test passed')
    else:
        print('Test failed')

medium

A. Replace print with return statements

B. Add parentheses around the if condition

C. Change '=' to '==' in the if condition

D. Remove else block

Solution

1. Step 1: Identify syntax error in if condition

   The single '=' is an assignment, not a comparison, causing a syntax error.

2. Step 2: Correct the comparison operator

   Replace '=' with '==' to compare values properly in the if statement.

3. Final Answer:

   Change '=' to '==' in the if condition -> Option C

4. Quick Check:

   Use '==' for comparison in if statements [OK]

Hint: Use '==' to compare, '=' assigns values [OK]

Common Mistakes:

• Using '=' instead of '==' in conditions
• Adding unnecessary parentheses in Python if
• Thinking print must be replaced with return

5. You updated your agent's decision logic. How should you design regression tests to ensure old behaviors remain correct while testing new features?

hard

A. Test randomly without expected outputs to save time

B. Only test new features since old ones worked before

C. Remove old tests to avoid conflicts with new logic

D. Create test cases for both old expected outputs and new expected outputs

Solution

1. Step 1: Understand regression test purpose

   Regression tests verify that old behaviors still work after changes.

2. Step 2: Design tests covering old and new behaviors

   Include test cases for old expected outputs and new expected outputs to check both.

3. Final Answer:

   Create test cases for both old expected outputs and new expected outputs -> Option D

4. Quick Check:

   Test old and new outputs to ensure full correctness [OK]

Hint: Test old and new cases to catch breaks early [OK]

Common Mistakes:

• Ignoring old tests after updates
• Deleting old tests to simplify
• Skipping expected outputs in tests