Why reasoning patterns determine agent capability in Agentic AI - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define a simple reasoning step for an agent.

Agentic AI

def reasoning_step(input_data):
    result = input_data [1] 2
    return result

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

A-

B+

C*

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using addition instead of multiplication

Using division which changes the scale incorrectly

The reasoning step multiplies the input by 2 to simulate a simple transformation.

2fill in blank

medium

Complete the code to check if the agent's reasoning output meets a threshold.

Agentic AI

def check_capability(output):
    if output [1] 10:
        return True
    else:
        return False

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

A>=

B<

C==

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using less than which reverses the logic

Using equality which is too strict

The agent is capable if the output is greater than or equal to 10.

3fill in blank

hard

Fix the error in the reasoning function to correctly combine two inputs.

Agentic AI

def combine_inputs(a, b):
    combined = a [1] b
    return combined

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

A//

B*

C-

D+

Attempts:

3 left

💡 Hint

Common Mistakes

Using multiplication which changes scale

Using floor division which loses precision

Combining inputs by addition is a common reasoning pattern to merge information.

4fill in blank

hard

Fill both blanks to create a reasoning pattern that filters and transforms data.

Agentic AI

def process_data(data):
    result = [x [1] 2 for x in data if x [2] 5]
    return result

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

A*

B>

C<

D+

Attempts:

3 left

💡 Hint

Common Mistakes

Using addition instead of multiplication

Using less than which filters wrong values

The function multiplies each item by 2 only if it is greater than 5, showing filtering and transformation.

5fill in blank

hard

Fill all three blanks to build a dictionary comprehension that maps inputs to their reasoning scores if above threshold.

Agentic AI

def reasoning_scores(inputs):
    scores = {{ [1]: [2] for [1] in inputs if [2] > 10 }}
    return scores

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

Aitem

Bscore

Citem * 2

Dvalue

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently

Filtering on wrong variable

The dictionary maps each item to its score (item * 2) only if the score is greater than 10.