Workflow Engine

Overview

SCANUE v22’s workflow engine is built on LangGraph, providing sophisticated orchestration capabilities for multi-agent systems. The engine manages complex workflows with conditional routing, state persistence, and human-in-the-loop integration.

Core Architecture

State Management

The workflow maintains persistent state across all execution phases:

Global State: Shared information accessible by all agents
Agent State: Private state maintained by individual agents
Transition State: Temporary data during workflow transitions

Conditional Routing

Dynamic workflow routing based on:

Agent Responses: Decisions based on agent output
External Conditions: Environmental or user-driven factors
Performance Metrics: Routing based on system performance

Error Handling

Comprehensive error management including:

Graceful Degradation: Continuing operation despite partial failures
Rollback Mechanisms: Reverting to previous stable states
Recovery Strategies: Automatic and manual recovery options

Workflow Components

Stages

The workflow is organized into distinct stages:

Initialization: System setup and agent preparation
Processing: Core task execution by agents
Coordination: Inter-agent communication and synchronization
Decision Points: Human-in-the-loop interaction opportunities
Finalization: Result compilation and cleanup

Transitions

Smooth transitions between stages with:

Validation: Ensuring prerequisites are met
State Transfer: Moving relevant data between stages
Cleanup: Releasing unnecessary resources

Example Workflow Structure

from workflow import SCANUEWorkflow
from agents.dlpfc import DLPFCAgent
from agents.specialized import SpecializedAgent

# Initialize workflow
workflow = SCANUEWorkflow()

# Define stages
workflow.add_stage("analysis", AnalysisStage())
workflow.add_stage("decision", DecisionStage())
workflow.add_stage("execution", ExecutionStage())

# Configure transitions
workflow.add_transition("analysis", "decision", condition="analysis_complete")
workflow.add_transition("decision", "execution", condition="decision_approved")

Human-in-the-Loop Integration

Interactive Decision Points

Strategic placement of human feedback opportunities:

Critical Decisions: High-impact choices requiring human judgment
Quality Gates: Human validation of intermediate results
Exception Handling: Human intervention for unexpected scenarios

Feedback Mechanisms

Real-time Input: Live interaction during workflow execution
Batch Feedback: Reviewing and approving multiple decisions
Asynchronous Review: Time-delayed feedback for non-urgent decisions

Implementation Example

from debug.demonstrate_hitl import setup_hitl_workflow

# Setup HITL workflow
workflow = setup_hitl_workflow()

# Add human decision point
workflow.add_human_checkpoint(
    stage="critical_decision",
    prompt="Please review the analysis results:",
    options=["approve", "modify", "reject"],
    timeout=300  # 5 minutes
)

Advanced Features

Parallel Execution

Execute multiple agents simultaneously:

# Parallel agent execution
workflow.execute_parallel([
    ("agent_1", task_1),
    ("agent_2", task_2),
    ("agent_3", task_3)
])

Dynamic Agent Selection

Choose agents based on runtime conditions:

# Dynamic agent selection
agent = workflow.select_agent(
    task_type="analysis",
    workload_level="high",
    expertise_required="statistical"
)

Workflow Composition

Combine multiple workflows:

# Compose workflows
main_workflow = SCANUEWorkflow()
sub_workflow = AnalysisWorkflow()

main_workflow.embed_workflow(sub_workflow, stage="analysis")

Monitoring and Debugging

Real-time Monitoring

Track workflow execution in real-time:

Stage Progress: Current stage and completion percentage
Agent Status: Individual agent states and activities
Performance Metrics: Execution time, resource usage, success rates

Debug Utilities

Comprehensive debugging tools:

Workflow Visualization: Graphical representation of workflow structure
State Inspection: Detailed view of workflow and agent states
Execution Traces: Step-by-step execution history

Debug Scripts

The system includes several debug utilities:

debug_workflow.py: General workflow debugging
debug_stage_transitions.py: Stage transition analysis
debug_langgraph_mapping.py: LangGraph integration debugging
demonstrate_hitl.py: Human-in-the-loop demonstration

Performance Optimization

Execution Strategies

Eager Execution: Immediate processing for time-critical tasks
Lazy Evaluation: Deferred processing for resource optimization
Batch Processing: Grouping similar tasks for efficiency

Resource Management

Memory Pooling: Efficient memory allocation and reuse
Connection Pooling: Optimized external service connections
Load Balancing: Distributing work across available resources

Caching Strategies

Result Caching: Storing computation results for reuse
State Caching: Persisting workflow states for quick recovery
Agent Model Caching: Caching trained models for faster initialization

Configuration

Workflow Configuration

workflow_config = {
    "max_execution_time": 3600,  # 1 hour
    "retry_attempts": 3,
    "parallel_execution": True,
    "human_timeout": 300,  # 5 minutes
    "state_persistence": True
}

workflow = SCANUEWorkflow(config=workflow_config)

Agent Configuration

agent_config = {
    "dlpfc_agent": {
        "memory_size": 1000,
        "attention_span": 10,
        "decision_threshold": 0.8
    },
    "specialized_agents": {
        "max_concurrent": 5,
        "timeout": 60
    }
}

Testing Workflows

Unit Testing

Test individual workflow components:

def test_stage_transition():
    workflow = SCANUEWorkflow()
    result = workflow.transition_to_stage("analysis")
    assert result.success == True

Integration Testing

Test complete workflow execution:

def test_full_workflow():
    workflow = setup_test_workflow()
    result = workflow.execute(test_input)
    assert result.completion_status == "success"

Performance Testing

Measure workflow performance:

def test_workflow_performance():
    workflow = SCANUEWorkflow()
    start_time = time.time()
    result = workflow.execute(large_dataset)
    execution_time = time.time() - start_time
    assert execution_time < 300  # 5 minutes

For more detailed information about workflow implementation, see our technical documentation.

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