Async agent execution in Agentic AI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Async Agent Execution Basics

Which statement best describes the main advantage of using asynchronous execution in AI agents?

A It allows the agent to perform multiple tasks at the same time without waiting for each to finish.

B It guarantees that tasks are executed in a strict order, one after another.

C It reduces the agent's memory usage by storing fewer task details.

D It makes the agent run slower but more accurately.

💡 Hint

Think about how multitasking works in everyday life, like cooking while listening to music.

Asynchronous execution lets agents handle multiple tasks simultaneously without waiting for each to complete, improving efficiency.

❓ Predict Output

intermediate

Output of Async Agent Task Scheduling

What will be the output of the following async agent code snippet?

Agentic AI

import asyncio

async def task(name, delay):
    await asyncio.sleep(delay)
    return f'Task {name} done'

async def main():
    results = await asyncio.gather(
        task('A', 1),
        task('B', 0.5),
        task('C', 0.2)
    )
    print(results)

asyncio.run(main())

A ['Task A done', 'Task C done', 'Task B done']

B ['Task C done', 'Task B done', 'Task A done']

C ['Task A done', 'Task B done', 'Task C done']

D ['Task B done', 'Task A done', 'Task C done']

💡 Hint

Consider how asyncio.gather returns results in the order of the tasks passed, not completion order.

asyncio.gather collects results in the order tasks are given, even if some finish earlier.

❓ Model Choice

advanced

Choosing the Best Model for Async Agent Execution

Which model architecture is best suited for an AI agent that requires efficient asynchronous task handling and context switching?

A Recurrent Neural Network (RNN) with sequential processing

B Transformer model with self-attention mechanisms

C Feedforward Neural Network with fixed input size

D Convolutional Neural Network (CNN) for image tasks

💡 Hint

Think about models that handle sequences flexibly and can focus on different parts of input simultaneously.

Transformer models use self-attention to manage context efficiently, making them ideal for async task handling.

❓ Hyperparameter

advanced

Hyperparameter Impact on Async Agent Performance

Which hyperparameter adjustment is most likely to improve an async agent's responsiveness when handling many simultaneous tasks?

A Increasing the number of parallel worker threads

B Reducing the learning rate drastically

C Decreasing the number of training epochs

D Increasing batch size during training

💡 Hint

Consider what helps an agent handle more tasks at the same time without delay.

More parallel worker threads allow the agent to process multiple tasks concurrently, improving responsiveness.

🔧 Debug

expert

Debugging Async Agent Deadlock

Given this async agent code, what is the cause of the deadlock preventing task completion?

Agentic AI

import asyncio

async def task1():
    await task2()
    return 'Task1 done'

async def task2():
    await task1()
    return 'Task2 done'

async def main():
    result = await asyncio.gather(task1(), task2())
    print(result)

asyncio.run(main())

A The tasks return strings instead of coroutine objects.

B The tasks are missing await keywords causing syntax errors.

C The asyncio.gather call is missing a timeout parameter.

D The tasks call each other recursively causing infinite waiting.

💡 Hint

Look for circular calls between async functions.

task1 awaits task2 and task2 awaits task1, causing a circular wait and deadlock.