Async agent execution in Agentic AI - ML Experiment: Train & Evaluate

Experiment - Async agent execution

Problem:You have an AI agent designed to perform multiple tasks sequentially, but it takes too long to complete all tasks.

Current Metrics:Total execution time: 120 seconds; Task success rate: 95%

Issue:The agent executes tasks one after another (synchronously), causing slow overall performance despite high task success.

Your Task

Modify the agent to execute tasks asynchronously to reduce total execution time while maintaining at least 90% task success rate.

Do not change the task logic or success criteria.

Keep the agent's environment and input data unchanged.

Hint 1

Hint 2

Hint 3

Solution

Agentic AI

import asyncio

class AsyncAgent:
    def __init__(self, tasks):
        self.tasks = tasks

    async def run_task(self, task_id, duration):
        print(f"Starting task {task_id}")
        await asyncio.sleep(duration)  # Simulate task work
        print(f"Finished task {task_id}")
        return f"Result of task {task_id}"

    async def run_all_tasks(self):
        # Create coroutine objects for all tasks
        coros = [self.run_task(i, d) for i, d in enumerate(self.tasks, 1)]
        # Run tasks concurrently and gather results
        results = await asyncio.gather(*coros)
        return results

# Example usage
async def main():
    # Simulate 5 tasks with different durations
    task_durations = [5, 10, 3, 7, 8]
    agent = AsyncAgent(task_durations)

    import time
    start_time = time.time()
    results = await agent.run_all_tasks()
    end_time = time.time()

    print("All tasks completed.")
    print("Results:", results)
    print(f"Total execution time: {end_time - start_time:.2f} seconds")

if __name__ == "__main__":
    asyncio.run(main())

Converted synchronous task execution to asynchronous using asyncio.

Used asyncio.gather to run all tasks concurrently.

Measured total execution time to verify speedup.

Results Interpretation

Before: Total execution time was 120 seconds with 95% task success rate.

After: Total execution time reduced to about 10 seconds with task success rate maintained at 95%.

Running tasks asynchronously can drastically reduce total execution time without sacrificing task success, demonstrating the power of concurrent execution in AI agents.

Bonus Experiment

Try implementing a timeout for each task so that if a task takes too long, it is cancelled and does not block other tasks.

💡 Hint

Use asyncio.wait_for to add a timeout to each task coroutine.

Practice

1. What is the main benefit of using async agent execution in AI systems?

easy

A. It makes the agents run slower but more accurately.

B. It allows multiple agents to run at the same time, speeding up processing.

C. It forces agents to run one after another in a fixed order.

D. It disables agents from communicating with each other.

Solution

1. Step 1: Understand async execution

   Async execution means running tasks without waiting for each to finish before starting the next.

2. Step 2: Apply to AI agents

   Running multiple AI agents at the same time speeds up overall processing by avoiding delays.

3. Final Answer:

   It allows multiple agents to run at the same time, speeding up processing. -> Option B

4. Quick Check:

   Async = concurrent execution = speed up [OK]

Hint: Async means agents run together, not one by one [OK]

Common Mistakes:

• Thinking async slows down agents
• Believing async forces sequential runs
• Confusing async with disabling communication

2. Which of the following is the correct syntax to run multiple async agents together in Python?

easy

A. await agent1() and agent2()

B. asyncio.run(agent1(), agent2())

C. await asyncio.gather(agent1(), agent2())

D. async gather(agent1, agent2)

Solution

1. Step 1: Recall asyncio syntax

   To run multiple async functions concurrently, use await asyncio.gather(...).

2. Step 2: Check options

   await asyncio.gather(agent1(), agent2()) uses correct syntax with await asyncio.gather(agent1(), agent2()). Others are invalid or incorrect.

3. Final Answer:

   await asyncio.gather(agent1(), agent2()) -> Option C

4. Quick Check:

   asyncio.gather + await = correct syntax [OK]

Hint: Use await with asyncio.gather to run agents together [OK]

Common Mistakes:

• Using asyncio.run with multiple args
• Missing await before asyncio.gather
• Wrong function call syntax without parentheses

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

import asyncio

async def agent1():
    await asyncio.sleep(1)
    return 'Agent1 done'

async def agent2():
    await asyncio.sleep(2)
    return 'Agent2 done'

async def main():
    results = await asyncio.gather(agent1(), agent2())
    print(results)

asyncio.run(main())

medium

A. ['Agent1 done', 'Agent2 done'] after about 2 seconds

B. ['Agent2 done', 'Agent1 done'] after about 2 seconds

C. ['Agent1 done', 'Agent2 done'] after about 3 seconds

D. Error because agent2 takes longer

Solution

1. Step 1: Understand asyncio.gather timing

   asyncio.gather runs tasks concurrently, so total time is max of individual times.

2. Step 2: Analyze sleep durations

   agent1 sleeps 1s, agent2 sleeps 2s, so total time ~2 seconds, results in order of calls.

3. Final Answer:

   ['Agent1 done', 'Agent2 done'] after about 2 seconds -> Option A

4. Quick Check:

   Concurrent run time = max sleep = 2s [OK]

Hint: Total time = longest agent sleep with asyncio.gather [OK]

Common Mistakes:

• Adding sleep times instead of taking max
• Assuming output order changes by sleep time
• Expecting error due to different sleep durations

4. What is wrong with this async agent execution code?

import asyncio

async def agent():
    return 'done'

async def main():
    results = asyncio.gather(agent(), agent())
    print(results)

asyncio.run(main())

medium

A. Missing await before asyncio.gather, so results is a coroutine, not actual results.

B. agent() is not async, so cannot be awaited.

C. asyncio.run cannot be used with async functions.

D. print cannot be used inside async functions.

Solution

1. Step 1: Check asyncio.gather usage

   asyncio.gather returns a coroutine; it must be awaited to get results.

2. Step 2: Identify missing await

   Code misses await before asyncio.gather, so print shows coroutine object, not results.

3. Final Answer:

   Missing await before asyncio.gather, so results is a coroutine, not actual results. -> Option A

4. Quick Check:

   Always await asyncio.gather to get results [OK]

Hint: Always put await before asyncio.gather to get results [OK]

Common Mistakes:

• Forgetting await before asyncio.gather
• Thinking print can't be used in async
• Misunderstanding asyncio.run usage

5. You want to run three async agents where agent3 depends on the results of agent1 and agent2. Which approach correctly handles this dependency using async agent execution?

hard

A. Run all three agents sequentially without async to ensure order.

B. Run agent3 concurrently with agent1 and agent2 using asyncio.gather without waiting.

C. Run agent3 first, then run agent1 and agent2 concurrently after.

D. Run agent1 and agent2 concurrently with asyncio.gather, await their results, then run agent3 with those results.

Solution

1. Step 1: Identify dependency order

   agent3 needs results from agent1 and agent2, so it must run after they finish.

2. Step 2: Use asyncio.gather for parallelism

   Run agent1 and agent2 concurrently with asyncio.gather, await results, then pass to agent3.

3. Final Answer:

   Run agent1 and agent2 concurrently with asyncio.gather, await their results, then run agent3 with those results. -> Option D

4. Quick Check:

   Run dependencies first, then dependent agent [OK]

Hint: Await dependencies before running dependent agent [OK]

Common Mistakes:

• Running dependent agent before dependencies finish
• Running all agents concurrently ignoring dependencies
• Running sequentially losing async speed benefits