Why frameworks accelerate agent development in Agentic AI - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the main framework module for agent development.

Agentic AI

import [1]

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

Amath

Bagent_framework

Crandom

Dos

Attempts:

3 left

💡 Hint

Common Mistakes

Importing unrelated modules like 'random' or 'os' instead of the framework.

The main framework module for agent development is 'agent_framework', which provides tools to build agents quickly.

2fill in blank

medium

Complete the code to create a new agent instance using the framework.

Agentic AI

agent = [1].Agent()

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Aagent_framework

Bmath

Cos

Drandom

Attempts:

3 left

💡 Hint

Common Mistakes

Using unrelated modules like 'random' or 'os' to create an agent.

To create an agent, you call the Agent class from the 'agent_framework' module.

3fill in blank

hard

Fix the error in the code to run the agent's main loop.

Agentic AI

agent.[1]()

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Arun

Bbegin

Cexecute

Dstart

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'start' or 'execute' which are not valid methods for the agent.

The correct method to run the agent's main loop is 'run()'.

4fill in blank

hard

Fill both blanks to define a simple agent behavior function and assign it.

Agentic AI

def behavior([1]):
    return [2]
agent.behavior = behavior

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Ainput_data

B'action_taken'

Coutput

D'done'

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect parameter names or returning wrong values.

The function takes 'input_data' and returns the string 'action_taken' as the agent's behavior.

5fill in blank

hard

Fill all three blanks to create a dictionary mapping agent states to actions with a condition.

Agentic AI

actions = [1]: [2] for [3] in states if [3] != 'idle'

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A{state

B'move'

Cstate

Dstates

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'states' as loop variable or missing the dictionary start brace.

The dictionary comprehension uses '{state' as the start, 'move' as the action, and 'state' as the loop variable.