Handling tool execution results in Agentic AI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to store the tool's output in a variable.

Agentic AI

tool_output = [1].execute(input_data)

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

Aexecute

Bresult

Ctool

Dinput_data

Attempts:

3 left

💡 Hint

Common Mistakes

Using the method name instead of the tool object.

Trying to assign input data instead of the output.

The tool object calls the execute method to run the tool and returns the output, which we store in tool_output.

2fill in blank

medium

Complete the code to check if the tool execution was successful.

Agentic AI

if tool_output.[1] == 'success':
    print('Tool ran successfully')

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

Amessage

Bresult

Coutput

Dstatus

Attempts:

3 left

💡 Hint

Common Mistakes

Checking the wrong attribute like message or output.

Comparing to wrong string values.

The status attribute usually tells if the tool ran successfully.

3fill in blank

hard

Fix the error in accessing the tool's output text.

Agentic AI

print(tool_output.[1]['text'])

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

Aresponse

Bresult

Coutput

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Using attributes that are strings instead of dictionaries.

Trying to access keys on non-dictionary attributes.

The tool's output text is stored inside the response attribute as a dictionary.

4fill in blank

hard

Fill both blanks to extract and print the tool's confidence score.

Agentic AI

confidence = tool_output.[1].get('[2]', 0)
print(f'Confidence: {confidence}')

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

Ametadata

Bconfidence

Cinfo

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong attribute names like info or confidence as attribute.

Using incorrect key names.

The confidence score is stored inside the metadata dictionary under the key score.

5fill in blank

hard

Fill all three blanks to handle tool output safely with error checking.

Agentic AI

if tool_output.[1] == 'error':
    print('Error:', tool_output.[2])
else:
    print('Result:', tool_output.[3]['text'])

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

Astatus

Bmessage

Cresponse

Doutput

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up attributes for status and message.

Trying to access text on wrong attribute.

Check status for errors, print the message if error, else print the text inside response.

Practice

1. What is the main reason an AI agent should carefully handle the results returned by a tool it uses?

easy

A. To reduce the size of the tool's code

B. To make the tool run faster

C. To ensure the agent makes correct decisions based on accurate information

D. To avoid using any external resources

Solution

1. Step 1: Understand the role of tool results in AI agents

   AI agents rely on tools to get extra information or perform tasks that help them decide what to do next.

2. Step 2: Recognize the importance of accurate results

   If the agent does not handle the tool's results carefully, it might make wrong decisions based on incorrect or incomplete data.

3. Final Answer:

   To ensure the agent makes correct decisions based on accurate information -> Option C

4. Quick Check:

   Handling results carefully = correct decisions [OK]

Hint: Focus on why accuracy matters for agent decisions [OK]

Common Mistakes:

• Thinking speed of tool matters more than result accuracy
• Ignoring the importance of result correctness
• Confusing tool code size with result handling

2. Which of the following is the correct way to check if a tool's execution result is empty in Python before using it?

easy

A. if result is None:

B. if result != None:

C. if result = None:

D. if result == None:

Solution

1. Step 1: Identify the correct syntax for None comparison in Python

   In Python, to check if a variable is None, use 'is None' instead of '==' because None is a singleton.

2. Step 2: Eliminate incorrect options

   if result == None: uses '==', which works but is not recommended. if result = None: uses '=' which is assignment, causing syntax error. if result != None: checks for not None, which is opposite.

3. Final Answer:

   if result is None: -> Option A

4. Quick Check:

   Use 'is None' to check None in Python [OK]

Hint: Use 'is None' to check for None, not '==' or '=' [OK]

Common Mistakes:

• Using '=' instead of '==' or 'is' causing syntax errors
• Using '==' instead of 'is' for None comparison
• Checking for not None when expecting None

3. Given the code below, what will be printed?

tool_result = {'status': 'success', 'data': [1, 2, 3]}
if tool_result.get('status') == 'success':
    print(len(tool_result['data']))
else:
    print(0)

medium

A. KeyError

B. 0

C. None

D. 3

Solution

1. Step 1: Check the status key in tool_result

   tool_result.get('status') returns 'success', so the if condition is True.

2. Step 2: Calculate length of data list

   tool_result['data'] is [1, 2, 3], which has length 3, so print(3) is executed.

3. Final Answer:

   3 -> Option D

4. Quick Check:

   Status is 'success', print length 3 [OK]

Hint: Check condition first, then count list length [OK]

Common Mistakes:

• Assuming else branch runs
• Confusing get() with direct key access
• Expecting KeyError when key exists

4. What is the error in the following code snippet that handles a tool's result?

result = tool.run()
if result != None:
    print(result['value'])
else:
    print('No result')

medium

A. Using '!=' instead of 'is not' to check None

B. Missing try-except block for key access

C. Using print instead of return

D. No error, code is correct

Solution

1. Step 1: Analyze None check

   Using 'result != None' works but 'result is not None' is preferred; this is not a critical error.

2. Step 2: Check key access safety

   Accessing result['value'] without checking if 'value' exists can cause KeyError if missing; no try-except or key check is present.

3. Final Answer:

   Missing try-except block for key access -> Option B

4. Quick Check:

   Always handle missing keys safely [OK]

Hint: Always check keys or catch exceptions when accessing dict values [OK]

Common Mistakes:

• Ignoring possible missing keys causing runtime errors
• Thinking '!=' None is always wrong
• Confusing print and return usage

5. An AI agent uses a tool that returns a dictionary with keys 'status' and 'output'. Sometimes 'output' can be an empty string or None. Which is the best way to handle the tool's result to safely get meaningful output or fallback to 'No data'?

hard

A. if result.get('status') == 'success' and result.get('output'):
use_output = result['output']
else:
use_output = 'No data'

B. if result['status'] == 'success' and result['output'] != '':
use_output = result['output']
else:
use_output = 'No data'

C. if result.get('status') == 'success' and result['output'] is not None:
use_output = result['output']
else:
use_output = 'No data'

D. if result['status'] == 'success' and result['output']:
use_output = result['output']
else:
use_output = 'No data'

Solution

1. Step 1: Use safe key access with get()

   Using result.get('status') avoids KeyError if 'status' is missing, making code safer.

2. Step 2: Check output truthiness to handle empty string or None

   Checking 'and result.get('output')' ensures output is not None or empty string, both falsy values, so fallback triggers correctly.

3. Final Answer:

   Option A -> Option A

4. Quick Check:

   Safe get() and truthy check handle missing or empty output [OK]

Hint: Use get() and check truthiness for safe, clean handling [OK]

Common Mistakes:

• Using direct key access risking KeyError
• Checking only for None but missing empty string case
• Not handling missing keys safely