Agent skill

agenticx-workflow-designer

Guide for designing and running AgenticX workflows including sequential pipelines, parallel execution, graph-based orchestration, conditional routing, and trigger services. Use when the user wants to create workflows, orchestrate multiple agents, design agent pipelines, or set up complex multi-step processes.

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Install this agent skill to your Project

npx add-skill https://github.com/DemonDamon/AgenticX/tree/main/agenticx/skills/agenticx-workflow-designer

Metadata

Additional technical details for this skill

author
AgenticX
version
0.3.6

SKILL.md

AgenticX Workflow Designer

Guide for building workflows that orchestrate agents, tasks, and execution paths.

Core Components

Component Purpose
Workflow Container for nodes and edges
WorkflowNode A step in the workflow (agent + task)
WorkflowEdge Connection between nodes (with optional conditions)
WorkflowEngine Runtime executor for the workflow graph
WorkflowGraph Graph representation of the workflow

Basic Workflow

python
from agenticx import Workflow, WorkflowNode, WorkflowEdge
from agenticx.core import WorkflowEngine

# Define nodes
research_node = WorkflowNode(
    id="research",
    agent=researcher_agent,
    task=research_task
)

analysis_node = WorkflowNode(
    id="analysis",
    agent=analyst_agent,
    task=analysis_task
)

# Define edges (sequential flow)
edge = WorkflowEdge(source="research", target="analysis")

# Build workflow
workflow = Workflow(
    id="research-pipeline",
    nodes=[research_node, analysis_node],
    edges=[edge]
)

# Execute
engine = WorkflowEngine()
result = engine.run(workflow)

CLI Workflow Creation

bash
# Create workflow scaffold
agx workflow create research-pipeline --agents "researcher,analyst"

# List workflows
agx workflow list

# Run a workflow file
agx run workflows/research-pipeline.py --verbose

Workflow Patterns

Sequential Pipeline

Nodes execute one after another:

[Research] → [Analysis] → [Report]
python
edges = [
    WorkflowEdge(source="research", target="analysis"),
    WorkflowEdge(source="analysis", target="report"),
]

Parallel Execution

Multiple nodes execute concurrently:

         ┌→ [Web Search] ─┐
[Start] ─┤                 ├→ [Merge]
         └→ [DB Query]   ─┘
python
edges = [
    WorkflowEdge(source="start", target="web-search"),
    WorkflowEdge(source="start", target="db-query"),
    WorkflowEdge(source="web-search", target="merge"),
    WorkflowEdge(source="db-query", target="merge"),
]

Conditional Routing

Route execution based on output:

python
edge = WorkflowEdge(
    source="classifier",
    target="handler-a",
    condition=lambda result: result.get("category") == "A"
)

Graph-Based Orchestration

For complex DAGs with multiple paths and merge points, use WorkflowGraph:

python
from agenticx.core import WorkflowGraph

graph = WorkflowGraph()
graph.add_node(research_node)
graph.add_node(analysis_node)
graph.add_node(report_node)
graph.add_edge("research", "analysis")
graph.add_edge("analysis", "report")

Triggers

Scheduled Trigger

python
from agenticx.core import TriggerService, ScheduledTrigger

trigger = ScheduledTrigger(
    cron="0 9 * * 1",  # Every Monday at 9am
    workflow_id="weekly-report"
)
service = TriggerService()
service.register(trigger)

Event-Driven Trigger

python
from agenticx.core import EventDrivenTrigger

trigger = EventDrivenTrigger(
    event_type="new_data_available",
    workflow_id="data-pipeline"
)

Execution Context

Track workflow state during execution:

python
from agenticx.core import ExecutionContext, WorkflowStatus

context = ExecutionContext(workflow_id="research-pipeline")
# context.status → WorkflowStatus.RUNNING / COMPLETED / FAILED
# context.node_results → dict of node_id → result

Running Workflow Files

bash
# Simple run
agx run my_workflow.py

# With config file
agx run my_workflow.py --config config.yaml --verbose

# Debug mode
agx run my_workflow.py --debug

Best Practices

  1. Start simple — begin with sequential, add complexity as needed
  2. Name nodes clearly — they appear in logs and monitoring
  3. Set timeouts — prevent infinite loops in conditional workflows
  4. Use validation — validate outputs at each node boundary
  5. Monitor execution — enable observability for production workflows

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