Workflow Management - joehubert/ai-agent-design-patterns GitHub Wiki

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Classification

Orchestration Pattern

Intent

To coordinate complex sequences of operations across multiple agents and tools with reliable state tracking, allowing for the management of multi-step processes while maintaining control over the execution flow.

Also Known As

Process Orchestration, Task Orchestration, Workflow Orchestration, Pipeline Management

Motivation

In complex AI systems that involve multiple agents, tools, and services, many tasks require coordinated sequences of operations that must be executed in a specific order with proper handling of dependencies and state. Traditional approaches often lack the structured coordination needed for reliable execution, especially when:

  • Operations have dependencies on previous steps
  • State must be tracked and maintained across multiple steps
  • Error handling and recovery mechanisms are required
  • Visibility into the process execution is needed
  • Complex branching logic determines the next steps

For example, a customer service AI might need to:

  1. Analyze a customer query
  2. Retrieve relevant customer data
  3. Search knowledge bases for solutions
  4. Generate a response
  5. Check the response for accuracy
  6. Submit the response to approval workflows if certain criteria are met
  7. Track the resolution status

Without proper workflow management, such processes become difficult to maintain, debug, and scale.

Applicability

Use the Workflow Management pattern when:

  • Tasks involve multiple sequential or parallel steps with dependencies
  • Operations span across different agents, tools, or services
  • Long-running processes need to be tracked and resumed
  • Complex conditional branching is required
  • Process state needs to be persisted
  • Operations require retries or fallback mechanisms
  • Auditing and visibility into process execution is important
  • Coordination between human and AI agents is needed

Structure

To do...

Components

  • Workflow Engine: The central coordinator that manages the execution of workflows, tracks state, and handles the transition between steps.

  • Workflow Definition: A declarative representation of the process, including steps, dependencies, conditions, and error handling strategies.

  • Task Executors: Components responsible for executing individual tasks within the workflow, which may include LLM agents, tool invocations, or external API calls.

  • State Store: A persistent storage mechanism that maintains the state of workflows, allowing for recovery and resumption of processes.

  • Event System: A mechanism for signaling the completion of tasks, triggering subsequent steps, and handling asynchronous operations.

  • Monitoring Component: Tools for tracking workflow execution, providing visibility, and collecting metrics.

  • Error Handling Subsystem: Mechanisms for detecting failures, implementing retry strategies, and managing fallback paths.

Interactions

The components interact in the following ways:

  1. The Workflow Engine loads a Workflow Definition at the start of execution.

  2. For each step in the workflow, the Engine determines if dependencies are satisfied and preconditions are met.

  3. When a step is ready for execution, the Engine dispatches the task to the appropriate Task Executor.

  4. Task Executors perform their assigned operations, which might include:

    • Prompting an LLM agent
    • Calling external APIs or tools
    • Retrieving or storing data
    • Making decisions based on predefined criteria

  5. Upon completion, Task Executors notify the Event System, which signals the Workflow Engine.

  6. The Workflow Engine updates the workflow state in the State Store and determines the next step(s) to execute.

  7. If errors occur, the Error Handling Subsystem implements appropriate recovery strategies.

  8. The Monitoring Component tracks the execution, collects metrics, and provides visibility.

  9. This cycle continues until the workflow completes or terminates due to errors or conditions.

Consequences

Benefits

  • Reliability: Explicit state tracking ensures operations complete even through interruptions
  • Scalability: Workflows can be distributed across computing resources
  • Visibility: Process execution can be monitored and audited
  • Maintainability: Complex processes are defined declaratively, making them easier to understand and modify
  • Error resilience: Built-in error handling improves system robustness
  • Reusability: Workflows can be templatized and reused across similar tasks

Limitations

  • Overhead: Adds complexity compared to simple sequential execution
  • Development cost: Requires initial investment in workflow infrastructure
  • Latency considerations: State persistence and coordination may add latency
  • Learning curve: Teams need to understand workflow patterns and tools

Performance implications

  • State persistence operations may introduce I/O overhead
  • Distributed execution can improve throughput but increases coordination complexity
  • Long-running workflows require efficient resource management

Implementation

To implement the Workflow Management pattern:

  1. Define your workflow model:

    • Choose between code-based or declarative workflow definitions
    • Design a schema for representing steps, dependencies, and conditions
    • Establish patterns for error handling and retries

  2. Select a state management approach:

    • Determine how workflow state will be persisted
    • Design the state transition model
    • Implement mechanisms for state recovery

  3. Create a workflow engine:

    • Build or adopt a workflow execution framework
    • Implement the logic for step sequencing and dependency resolution
    • Design the task dispatch mechanism

  4. Implement task executors:

    • Create standardized interfaces for task execution
    • Build connectors for LLM agents, tools, and external services
    • Implement error reporting mechanisms

  5. Design the event system:

    • Establish patterns for signaling task completion
    • Implement event subscriptions for workflow progression
    • Create mechanisms for timeout handling

  6. Build monitoring capabilities:

    • Implement logging for workflow execution
    • Create dashboards for visualization
    • Design alerting for workflow failures

  7. Test workflows extensively:

    • Verify error handling works as expected
    • Test recovery from interruptions
    • Evaluate performance under load

Common pitfalls to avoid:

  • Over-engineering workflows for simple tasks
  • Insufficient error handling and retry logic
  • Storing too much state data, creating performance bottlenecks
  • Tight coupling between workflow steps, reducing flexibility

Code Examples

To do...

Variations

Event-driven Workflows

Rather than sequential execution, these workflows progress based on events from various sources. They're useful for systems that need to react to external stimuli or user actions.

Human-in-the-loop Workflows

These incorporate explicit approval or decision points where human operators must intervene. They're essential for high-stakes domains or regulatory compliance.

Distributed Workflows

These divide execution across multiple machines or services, improving scalability but increasing coordination complexity.

Dynamic Workflows

Unlike static workflows with predefined steps, these workflows can modify their structure during execution based on runtime conditions or AI planning.

Microbatch Workflows

These process data in small batches rather than as individual items, optimizing throughput for data-intensive applications.

Real-World Examples

  • Customer Support Systems: Workflows managing the entire lifecycle of support tickets, from initial classification to resolution verification.

  • Content Moderation Platforms: Multi-stage workflows that combine AI analysis, human review, and appeals processes.

  • Research Assistant Agents: Workflows coordinating information gathering, synthesis, citation collection, and summary generation.

  • Multi-agent Collaboration Systems: Workflows orchestrating specialized agents working together on complex tasks like code generation and review.

  • Document Processing Systems: Workflows managing extraction, verification, approval, and storage of information from documents.

Related Patterns

  • Planner Pattern: Often used to generate workflow definitions dynamically based on task requirements.

  • ReAct Pattern: Can be embedded within workflow steps to provide structured reasoning and action capabilities.

  • Asynchronous Processing Pattern: Frequently used together with Workflow Management to handle long-running operations.

  • Hierarchical Task Decomposition: Works with Workflow Management to handle complex nested processes.

  • Router Pattern: Often feeds into workflows after determining the appropriate processing path.

  • Fallback Chains: Can be implemented within workflows to provide resilience when primary approaches fail.