Why observability is critical for agents in Agentic Ai - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to print the agent's current state for observability.

Agentic_ai

print('Agent state:', [1])

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

Aagent.state

Bagent.run()

Cagent.execute()

Dagent.action

Attempts:

3 left

2fill in blank

medium

Complete the code to log the agent's decision for observability.

Agentic_ai

log_entry = {'decision': [1]
logger.log(log_entry)

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

Aagent.input

Bagent.state

Cagent.memory

Dagent.last_action

Attempts:

3 left

3fill in blank

hard

Fix the error in the code to correctly track the agent's performance metric.

Agentic_ai

performance = agent.metrics.get([1], 0)

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

A'accuracy'

Baccuracy

C'loss'

Dloss

Attempts:

3 left

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that records agent states with timestamps.

Agentic_ai

state_log = {timestamp: [1] for timestamp, [2] in agent.history.items()}

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

Astate

Caction

Devent

Attempts:

3 left

5fill in blank

hard

Fill all three blanks to filter and store agent actions with confidence above threshold.

Agentic_ai

filtered_actions = [1]: [2] for [3], [2] in agent.actions.items() if [2]['confidence'] > 0.8}

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

Aaction_id

Baction

Attempts:

3 left