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Asynchronous function execution

4 min read

From: aidotengineer

Asynchronous function execution is a crucial capability for modern AI agents, enabling them to handle long-running operations without blocking the user interface or conversational flow. This approach allows agents to perform multiple tasks concurrently, improving responsiveness and user experience [03:43:02].

The Problem: Blocking Operations [00:20:20]

Traditionally, AI agent loops often involve a sequence where the model generates a response, potentially including a function call, and then waits for the function to execute and return a result before generating the next part of the conversation [00:20:20]. While this synchronous approach is straightforward for quick operations, it leads to a poor user experience when function calls involve significant delays, such as network requests or complex computations [03:52:18]. The user is left waiting for the agent to respond, creating a noticeable lag [03:52:22].

The Solution: Asynchronous Execution [04:05:05]

To overcome blocking, asynchronous function execution allows the agent to initiate a function call and continue processing other requests or interacting with the user without waiting for the call’s immediate completion [04:17:17]. When the asynchronous function eventually returns, its result is then integrated back into the conversation or agent’s state [04:19:50].

Implementation with asyncio [04:38:00]

In Python, the asyncio library is instrumental in building asynchronous systems [04:37:40]. Key components of an asynchronous agent include:

  • Non-Blocking Operations: Functions that involve waiting (like network calls or deliberate sleep functions) should be designed using async/await keywords. For instance, asyncio.sleep can be used instead of time.sleep to allow the program to perform other tasks during the wait [05:28:53].
  • Parallel Tool Execution: The agent can initiate multiple tool calls concurrently using asyncio.gather (or similar constructs). This allows all long-running operations to run in parallel, significantly reducing overall waiting time, especially for network-bound tasks [04:48:00].
  • Message Queues: To manage conversation flow and prevent conflicting generations from parallel operations, a message queue can be used. User inputs and function results are added to this queue, and the agent processes them sequentially, ensuring the conversational history remains consistent [04:46:00].

Task Management and Delegation [05:26:00]

A common pattern for Asynchronous function execution is to introduce a “task” concept:

  1. Create Task Function (create_task): When the agent needs to perform a long-running operation (e.g., calling a model for a complex request), it calls a create_task function. This function initiates the operation in the background and returns a unique task ID to the model [05:51:00].
  2. Check Task Function (check_task or check_all_tasks): The user or the agent can then call a check_task function (or check_all_tasks for multiple concurrent operations) to inquire about the status or retrieve the result of a specific background task. This allows for continuous interaction with the agent while tasks are pending [05:52:00].

This pattern facilitates agent orchestration and parallel processing, as the main agent can delegate tasks to other models or processes without blocking its own operation [03:30:00]. This means the agent can keep chatting with the user or spin off additional tasks, handling multiple requests concurrently [01:00:44].

Example: Asynchronous Weather Retrieval [05:21:00]

Consider a scenario where an agent needs to retrieve weather information for multiple cities.

  • Synchronous: If each weather query takes 1 second, querying 5 cities synchronously would take 5 seconds [05:37:00].
  • Asynchronous: By using asyncio.sleep (mimicking a network call) within the weather function and handling calls in parallel, the total time for querying 5 cities can be reduced to just over 1 second, as all calls happen concurrently [05:33:00].

The agent initiates all weather requests in parallel and aggregates the results, demonstrating efficient use of non-blocking I/O [05:35:00].

Asynchronous Capabilities in Real-Time APIs [03:32:00]

Some advanced AI APIs, like OpenAI’s Real-Time API, are designed to natively support asynchronous function execution. This means the model can call a function, receive no immediate response, and the conversation can continue until a response is eventually available. This is particularly critical for real-time applications where halting the conversation for every function call is not feasible [01:39:50]. This native capability helps prevent conversation stalls, making interactions more fluid and natural [01:40:02].

Conclusion

Asynchronous function execution is a powerful paradigm for building robust and responsive AI agents. By carefully designing functions to be non-blocking and implementing a task management system, developers can create agents that handle complex, long-running operations efficiently while maintaining a smooth user experience [05:55:00].

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