Measuring agent accuracy and relevance in Agentic AI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

Agentic AI

accuracy = correct_predictions / [1]

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

Awrong_predictions

Btotal_agents

Ctotal_predictions

Dcorrect_predictions

Attempts:

3 left

💡 Hint

Common Mistakes

Dividing by wrong_predictions instead of total_predictions.

Using correct_predictions twice in the formula.

Accuracy is the ratio of correct predictions to the total number of predictions made.

2fill in blank

medium

Complete the code to compute the relevance score using cosine similarity.

Agentic AI

relevance_score = cosine_similarity(agent_vector, [1])

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

Aquery_vector

Brandom_vector

Cagent_vector

Dnoise_vector

Attempts:

3 left

💡 Hint

Common Mistakes

Comparing the agent vector to itself instead of the query vector.

Using a random or noise vector which does not relate to the query.

Relevance is measured by comparing the agent's output vector to the query vector using cosine similarity.

3fill in blank

hard

Fix the error in the code that calculates F1 score from precision and recall.

Agentic AI

f1_score = 2 * (precision * recall) / [1]

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

Aprecision + recall

Bprecision - recall

Cprecision * recall

Dprecision / recall

Attempts:

3 left

💡 Hint

Common Mistakes

Using subtraction or multiplication in the denominator instead of addition.

Dividing precision by recall instead of adding them.

The F1 score formula divides twice the product of precision and recall by their sum.

4fill in blank

hard

Fill both blanks to create a dictionary of agent accuracies for agents with accuracy above 0.8.

Agentic AI

accuracies = {agent: [1] for agent, [2] in results.items() if accuracy > 0.8}

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

Aaccuracy

Bscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using a variable name other than accuracy in the loop unpacking.

Not using accuracy as the dictionary value.

The dictionary comprehension extracts accuracy for each agent and filters those with accuracy above 0.8.

5fill in blank

hard

Fill all three blanks to filter agents with relevance above 0.7 and create a summary dictionary.

Agentic AI

summary = {agent: [1] for agent, [2] in agent_results.items() if [3] > 0.7}

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

Arelevance

Daccuracy

Attempts:

3 left

💡 Hint

Common Mistakes

Using accuracy instead of relevance in any blank.

Using different variable names inconsistently.

The dictionary comprehension extracts relevance scores for agents and filters those with relevance above 0.7.

Practice

1. What does accuracy measure when evaluating an AI agent's answers?

easy

A. How many answers are related but not exact

B. How fast the agent responds

C. How many answers are exactly correct

D. How many answers are generated

Solution

1. Step 1: Understand accuracy definition

   Accuracy counts the number of answers that match the correct ones exactly.

2. Step 2: Compare with other metrics

   Relevance measures usefulness, not exact correctness, so it is different from accuracy.

3. Final Answer:

   How many answers are exactly correct -> Option C

4. Quick Check:

   Accuracy = exact correctness [OK]

Hint: Accuracy means exact right answers only [OK]

Common Mistakes:

• Confusing accuracy with relevance
• Thinking accuracy measures speed
• Assuming accuracy counts all related answers

2. Which of the following is the correct way to calculate accuracy for an AI agent's answers?

easy

A. Number of related answers divided by total answers

B. Number of correct answers divided by total answers

C. Number of answers generated per second

D. Number of answers ignored by the agent

Solution

1. Step 1: Recall accuracy formula

   Accuracy = (correct answers) / (total answers given).

2. Step 2: Eliminate incorrect options

   Options about related answers or speed do not define accuracy.

3. Final Answer:

   Number of correct answers divided by total answers -> Option B

4. Quick Check:

   Accuracy = correct / total [OK]

Hint: Accuracy = correct answers ÷ total answers [OK]

Common Mistakes:

• Using related answers count instead of correct
• Mixing speed with accuracy
• Ignoring total number of answers

3. Given an AI agent answered 80 questions, 60 were exactly correct, and 10 more were relevant but not exact. What is the accuracy and relevance percentage?

medium

A. Accuracy 60%, Relevance 70%

B. Accuracy 60%, Relevance 87.5%

C. Accuracy 75%, Relevance 60%

D. Accuracy 75%, Relevance 87.5%

Solution

1. Step 1: Calculate accuracy percentage

   Accuracy = (60 correct / 80 total) * 100 = 75%.

2. Step 2: Calculate relevance percentage

   Relevance = ((60 correct + 10 relevant) / 80 total) * 100 = 87.5%.

3. Final Answer:

   Accuracy 75%, Relevance 87.5% -> Option D

4. Quick Check:

   Accuracy = 75%, Relevance = 87.5% [OK]

Hint: Add relevant to correct for relevance % [OK]

Common Mistakes:

• Mixing accuracy and relevance values
• Not adding relevant answers for relevance
• Dividing by wrong total number

4. An AI agent evaluation code snippet is below. It calculates accuracy but returns 0. What is the bug?

correct = 50
total = 0
accuracy = correct / total
print(accuracy)

medium

A. Division by zero error due to total being zero

B. Correct variable is zero, so accuracy is zero

C. Print statement syntax is wrong

D. Accuracy should be multiplied by 100

Solution

1. Step 1: Identify variables and operation

   correct = 50, total = 0, accuracy = correct / total.

2. Step 2: Check for division errors

   Dividing by zero (total=0) causes an error or invalid result.

3. Final Answer:

   Division by zero error due to total being zero -> Option A

4. Quick Check:

   Division by zero causes error [OK]

Hint: Check denominator is not zero before dividing [OK]

Common Mistakes:

• Ignoring zero division error
• Thinking print syntax is wrong
• Assuming accuracy must be multiplied by 100

5. You want to improve an AI agent's trust by measuring both accuracy and relevance. Which approach best helps achieve this?

hard

A. Track exact correct answers and also count useful related answers

B. Only count answers that are exactly correct

C. Ignore relevance and focus on speed of answers

D. Count all answers regardless of correctness or relevance

Solution

1. Step 1: Understand trust factors

   Trust improves when answers are both correct and useful (relevant).

2. Step 2: Choose measurement approach

   Tracking both exact correctness (accuracy) and usefulness (relevance) gives a fuller picture.

3. Final Answer:

   Track exact correct answers and also count useful related answers -> Option A

4. Quick Check:

   Measure accuracy + relevance for trust [OK]

Hint: Measure both exact and useful answers for trust [OK]

Common Mistakes:

• Focusing only on exact correctness
• Ignoring relevance completely
• Measuring speed instead of quality