Asynchronous Processing - joehubert/ai-agent-design-patterns GitHub Wiki

Home::Overview of Patterns

Classification

Orchestration Pattern

Intent

To manage long-running operations while maintaining system responsiveness through non-blocking execution models, allowing agents to initiate tasks and continue processing without waiting for task completion.

Also Known As

  • Non-Blocking Processing
  • Event-Driven Processing
  • Background Processing
  • Concurrent Task Management

Motivation

In agentic AI applications, certain operations can be time-consuming, such as:

  • Large-scale data processing
  • External API calls with variable latency
  • Complex computations or simulations
  • Multi-step reasoning chains requiring significant computation

Traditional synchronous processing approaches force the system to wait for each operation to complete before proceeding, resulting in:

  • Poor user experience due to long wait times
  • Inefficient resource utilization
  • Scalability limitations
  • Potential timeouts for operations exceeding allowed execution windows

The Asynchronous Processing pattern addresses these challenges by allowing operations to be initiated and then processed independently of the main execution flow, enabling the system to remain responsive while complex or time-intensive tasks are completed in the background.

Applicability

Use the Asynchronous Processing pattern when:

  • Operations may have unpredictable or long execution times
  • System responsiveness is a critical requirement
  • Tasks can be logically decoupled and don't require immediate results
  • Processing needs to continue even if a particular operation fails
  • The application needs to handle many concurrent operations
  • You're working with rate-limited external APIs or services
  • Complex, multi-step agent workflows exceed standard timeout limits
  • Users expect continuous feedback during lengthy operations

Structure

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Components

  • Task Initiator: Responsible for initiating asynchronous operations, typically an LLM agent or orchestration component that identifies when an operation should be performed asynchronously.

  • Task Queue: A persistent storage mechanism that holds tasks waiting to be processed, maintaining their state, parameters, and execution context.

  • Worker Pool: A collection of processing units that execute tasks from the queue, potentially running on separate threads, processes, or even separate machines.

  • Result Store: A mechanism for storing the results of completed tasks until they are retrieved by interested components.

  • Callback Handler: Processes the results when asynchronous operations complete, updating system state or triggering subsequent actions.

  • Status Monitor: Tracks the progress of asynchronous operations, providing feedback to users or other system components.

  • Error Handler: Manages failures in asynchronous operations, implementing retry logic, fallback strategies, or error reporting.

Interactions

  1. The Task Initiator identifies an operation that should be performed asynchronously and creates a task with appropriate parameters and context.

  2. The task is added to the Task Queue with a unique identifier and initial status.

  3. Available Workers from the Worker Pool continuously poll the queue or receive notifications about new tasks.

  4. A Worker retrieves a task, updates its status to "in progress," and begins execution.

  5. During execution, the Status Monitor provides updates on task progress to interested components.

  6. Upon completion, the Worker stores the result in the Result Store and updates the task status.

  7. The Callback Handler is notified of the completion and processes the result according to the original task's requirements.

  8. If errors occur, the Error Handler manages the situation according to predefined policies.

  9. The Task Initiator or other interested components can check task status or retrieve results as needed.

Consequences

Benefits

  • Improved Responsiveness: The system can immediately acknowledge requests and continue processing other tasks while long-running operations execute in the background.
  • Better Resource Utilization: Processing resources can be distributed more efficiently across multiple tasks.
  • Enhanced Scalability: The system can handle more concurrent operations by queuing tasks and processing them when resources become available.
  • Failure Isolation: Failures in asynchronous operations don't immediately impact the main processing flow.
  • Operational Flexibility: Long-running operations can exceed the timeout limits of synchronous requests.
  • Workload Balancing: Task distribution can be optimized based on resource availability and priority.

Limitations

  • Increased Complexity: Asynchronous systems are inherently more complex to design, implement, and debug.
  • State Management Challenges: Maintaining context and state across asynchronous boundaries requires careful design.
  • Eventual Consistency: Results are not immediately available, requiring systems to handle eventual consistency.
  • Monitoring Overhead: Tracking the status of many concurrent operations adds overhead.
  • Error Handling Complexity: Failures in asynchronous operations require specialized error handling strategies.
  • Potential for Stuck Tasks: Without proper timeouts and monitoring, tasks may become stuck in the system.

Performance Implications

  • Reduces peak resource consumption by distributing workloads over time
  • May increase overall latency for individual operations due to queuing
  • Introduces overhead for task serialization, queuing, and result retrieval
  • Can improve throughput for systems handling many operations concurrently

Implementation

  1. Choose an Appropriate Queuing Mechanism:

    • Message queues (e.g., RabbitMQ, Kafka, SQS)
    • Database-backed queues
    • In-memory queues with persistence
    • Distributed task systems (e.g., Celery, Temporal)

  2. Define Task Representation:

    • Unique identifier
    • Input parameters
    • Execution context
    • Priority and dependencies
    • Timeout and retry policies

  3. Implement Worker Management:

    • Auto-scaling based on queue depth
    • Resource allocation strategies
    • Health monitoring and worker replacement
    • Graceful shutdown mechanisms

  4. Design Result Handling:

    • Polling vs. callback mechanisms
    • Result expiration policies
    • Partial result handling

  5. Establish Monitoring and Observability:

    • Task status visibility
    • Progress indicators
    • Execution metrics
    • Dead letter queues for failed tasks

  6. Implement Error Handling:

    • Retry policies with exponential backoff
    • Circuit breakers for failing dependencies
    • Fallback strategies for unrecoverable errors
    • Logging and alerting mechanisms

  7. Consider Task Prioritization:

    • Priority queues for critical operations
    • Fair scheduling to prevent starvation
    • Resource reservation for high-priority tasks

Code Examples

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Variations

Event-Driven Asynchronous Processing

  • Uses events to trigger tasks and handle completions
  • More loosely coupled than direct callback mechanisms
  • Well-suited for complex workflows with multiple interested components
  • Enables reactive programming models for agentic applications

Scheduled Asynchronous Processing

  • Tasks are scheduled for future execution rather than immediate processing
  • Useful for recurring operations or operations that should happen at specific times
  • Can implement complex scheduling policies (e.g., cron-like expressions)
  • Supports batch processing and resource optimization

Stream Processing

  • Processes continuous streams of data incrementally
  • Handles potentially infinite data streams
  • Produces incremental results that can be consumed as they become available
  • Well-suited for real-time analytics and continuous monitoring

Long Polling

  • A hybrid approach where clients wait for results but with extended timeouts
  • Simpler to implement than full asynchronous processing for some scenarios
  • Reduces the number of status check requests
  • Appropriate for operations with moderate execution times

Real-World Examples

  • OpenAI GPT Function Calling: Implements asynchronous processing for function executions that may take longer than the API timeout, allowing complex tool use without blocking the main conversation flow.

  • LangChain's AsyncCallbackManager: Provides infrastructure for asynchronous handling of callbacks in LLM application workflows, enabling non-blocking chain execution.

  • AutoGPT's Task Queue: Manages long-running autonomous agent tasks that involve multiple steps and external tool usage, allowing agents to work on multiple objectives concurrently.

  • LlamaIndex's Async Query Engine: Supports asynchronous query processing against large knowledge bases, enabling responsive interfaces while complex retrievals occur in the background.

  • HuggingFace's Inference Endpoints: Implement asynchronous inference for large models, allowing clients to submit requests and retrieve results later without maintaining long-lived connections.

Related Patterns

  • Workflow Management: Often used alongside Asynchronous Processing to coordinate complex sequences of asynchronous operations.

  • Router Pattern: Can direct different types of tasks to appropriate asynchronous processing pipelines based on task characteristics.

  • Fallback Chains: Complement asynchronous processing by providing alternative execution paths when asynchronous operations fail.

  • Semantic Caching: Can reduce the need for asynchronous processing by serving cached results for similar queries.

  • Hierarchical Task Decomposition: Often leverages asynchronous processing to execute sub-tasks independently.

  • Tool Use Pattern: Frequently implements asynchronous processing for external tool interactions to handle variable latency.