Why evaluation ensures agent reliability in Agentic AI - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the accuracy of an agent's predictions.

Agentic AI

accuracy = sum(predictions == [1]) / len(predictions)

Drag options to blanks, or click blank then click option'

Alabels

Bpredictions

Coutputs

Dresults

Attempts:

3 left

💡 Hint

Common Mistakes

Using predictions instead of labels for comparison.

The accuracy is calculated by comparing the agent's predictions to the true labels.

2fill in blank

medium

Complete the code to split data into training and testing sets.

Agentic AI

train_data, test_data = data[:[1]], data[[1]:]

Drag options to blanks, or click blank then click option'

A0.7

B0.3

Cint(len(data) * 0.7)

Dlen(data)

Attempts:

3 left

💡 Hint

Common Mistakes

Using float 0.7 directly as index causes error.

We split data at 70% length to create training and testing sets.

3fill in blank

hard

Fix the error in the code that calculates the mean squared error (MSE).

Agentic AI

mse = sum((predictions - [1]) ** 2) / len(predictions)

Drag options to blanks, or click blank then click option'

Alabels

Bpredictions

Coutputs

Dresults

Attempts:

3 left

💡 Hint

Common Mistakes

Subtracting predictions from predictions results in zero error.

MSE compares predictions to true labels to measure error.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 letters.

Agentic AI

{word: [1] for word in words if len(word) [2] 3}

Drag options to blanks, or click blank then click option'

Alen(word)

Bword.upper()

C>

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using word.upper() instead of length.

Using '<=' instead of '>'.

The dictionary maps each word to its length only if the word is longer than 3 letters.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary with uppercase keys and values greater than 0.

Agentic AI

result = { [1]: [2] for k, v in data.items() if v [3] 0 }

Drag options to blanks, or click blank then click option'

Ak.upper()

Bv

C>

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using k.lower() instead of k.upper().

Using '<' instead of '>'.

This creates a dictionary with keys in uppercase and values only if they are greater than zero.

Practice

1. Why is evaluation important for an AI agent's reliability?

easy

A. It tests the agent on new data to check if it makes good decisions.

B. It increases the agent's speed during training.

C. It changes the agent's internal code automatically.

D. It removes all errors from the agent's data.

Solution

1. Step 1: Understand evaluation purpose

   Evaluation tests how well the agent performs on data it has not seen before.

2. Step 2: Connect evaluation to reliability

   By testing on new data, evaluation shows if the agent can make good decisions consistently.

3. Final Answer:

   It tests the agent on new data to check if it makes good decisions. -> Option A

4. Quick Check:

   Evaluation = test on new data [OK]

Hint: Evaluation checks agent decisions on new data [OK]

Common Mistakes:

• Thinking evaluation speeds up training
• Believing evaluation changes agent code
• Assuming evaluation removes data errors

2. Which of the following is the correct way to evaluate an agent's performance?

easy

A. Train the agent and test it on the same data.

B. Test the agent on new, unseen data after training.

C. Only check the agent's code without running it.

D. Skip testing if training accuracy is high.

Solution

1. Step 1: Identify proper evaluation method

   Evaluation requires testing on data the agent has not seen during training.

2. Step 2: Eliminate incorrect options

   Testing on training data or skipping testing does not ensure reliability.

3. Final Answer:

   Test the agent on new, unseen data after training. -> Option B

4. Quick Check:

   Evaluation = test on unseen data [OK]

Hint: Always test on new data, not training data [OK]

Common Mistakes:

• Testing on training data only
• Ignoring testing if training looks good
• Checking code without running

3. Consider this code snippet evaluating an agent's accuracy:

agent_accuracy = agent.evaluate(test_data)
print(f"Accuracy: {agent_accuracy:.2f}")

What does this output represent?

medium

A. The agent's training loss value.

B. The agent's accuracy on training data.

C. The agent's accuracy on test data.

D. The agent's speed during evaluation.

Solution

1. Step 1: Understand the code context

   The method agent.evaluate(test_data) runs the agent on test data, not training data.

2. Step 2: Interpret the printed result

   The printed accuracy shows how well the agent performs on the test data.

3. Final Answer:

   The agent's accuracy on test data. -> Option C

4. Quick Check:

   Evaluate(test_data) = test accuracy [OK]

Hint: Evaluate method uses test data for accuracy [OK]

Common Mistakes:

• Confusing test data with training data
• Thinking output is loss instead of accuracy
• Assuming output shows speed

4. This code tries to evaluate an agent but causes an error:

accuracy = agent.evaluate(training_data)
print(f"Accuracy: {accuracy}")

What is the main problem here?

medium

A. The agent object cannot call evaluate method.

B. The print statement syntax is incorrect.

C. The variable 'accuracy' is not defined before use.

D. Evaluating on training data does not test reliability properly.

Solution

1. Step 1: Check evaluation data choice

   Using training data for evaluation does not measure how well the agent generalizes.

2. Step 2: Confirm code correctness

   Print syntax and variable usage are correct; agent likely supports evaluate method.

3. Final Answer:

   Evaluating on training data does not test reliability properly. -> Option D

4. Quick Check:

   Evaluation must use new data [OK]

Hint: Evaluate on new data, not training data [OK]

Common Mistakes:

• Thinking print syntax is wrong
• Assuming variable undefined
• Believing agent lacks evaluate method

5. An agent was evaluated on two datasets: test_data1 and test_data2. It scored 90% accuracy on test_data1 but only 60% on test_data2. What does this tell us about the agent's reliability?

hard

A. The agent may be overfitting and not reliable on all data.

B. The agent's training was perfect.

C. The agent is reliable on all data equally.

D. The evaluation method is incorrect.

Solution

1. Step 1: Compare accuracy on different test sets

   High accuracy on one test set but low on another suggests inconsistent performance.

2. Step 2: Understand overfitting impact

   The agent likely learned specifics of one dataset but fails to generalize to others.

3. Final Answer:

   The agent may be overfitting and not reliable on all data. -> Option A

4. Quick Check:

   Different accuracies = possible overfitting [OK]

Hint: Big accuracy gaps hint at overfitting [OK]

Common Mistakes:

• Assuming agent is reliable everywhere
• Thinking training was perfect from test scores
• Blaming evaluation method instead of agent