Workforce Hub
Build sophisticated multi-agent teams with AgenticFlow's visual Workforce orchestration system. Our most powerful feature.
What is Workforce?
Workforce is AgenticFlow's flagship feature that enables you to:
Orchestrate multiple AI agents working together
Visually design agent interactions with React Flow
Create complex workflows with agent collaboration
Route dynamically between specialized agents
Coordinate tasks across agent teams
Scale intelligently with parallel execution
Why Workforce?
Traditional workflows are sequential and deterministic. Individual agents are conversational but work alone. Workforce combines the power of both: multiple intelligent agents collaborating to solve complex problems.
When to Use Workforce
Perfect for:
Complex multi-step tasks requiring different skills
Agent specialization and collaboration
Dynamic routing based on context
Parallel agent execution
Advanced automation requiring reasoning
Sophisticated decision-making workflows
Use Agents instead when:
Single conversational interface is enough
No need for agent collaboration
Simple Q&A or chat
Use Workflows instead when:
Sequential automation is sufficient
No AI reasoning needed per step
Simple integrations
Getting Started
Deploy your first multi-agent team in 15 minutes.
Understand the concepts behind Workforce.
Visual Builder
Learn the React Flow-based visual interface.
Understanding Workforce nodes:
Agent Nodes - Individual AI agents with specific roles
Router Nodes - Dynamic routing logic
Merger Nodes - Combine outputs from multiple agents
Condition Nodes - Branching logic
Tool Nodes - Execute MCP tools
Data Nodes - Transform and process data
Input/Output Nodes - System boundaries
How to connect nodes and control execution flow.
Orchestration Patterns
Sequential Chain:
Agent A → Agent B → Agent C → OutputEach agent builds on the previous agent's work.
Parallel Processing:
→ Agent A →
Input → → Agent B → → Merger → Output
→ Agent C →Multiple agents work simultaneously, results combined.
Hierarchical (Supervisor):
Supervisor Agent
/ | \
Agent A Agent B Agent COne agent coordinates and delegates to others.
Dynamic Routing:
Input → Router → [Agent A or B or C] → OutputIntelligent routing based on input analysis.
Recursive (Self-Improving):
Input → Agent → Evaluate → [Complete or Loop Back] → OutputAgent refines its own output until quality threshold met.
How agents share information and context.
Advanced Features
Enterprise capabilities including:
Dynamic Routing - Intelligent agent selection based on input analysis, agent expertise, load balancing, and business rules
Parallel Processing - Execute multiple agents simultaneously with resource optimization and result aggregation
Error Recovery - Graceful handling of agent failures with retry logic and fallback agents
Loop Support - Iterative processes and continuous improvement
Version Control - Professional workflow management
Smart Node Palette - AI-assisted workflow construction
Execution Monitoring - Real-time performance analytics
Workforce Templates
Start with pre-built multi-agent teams:
Popular Templates:
Customer Service Team - Triage → Specialist → Escalation
Content Creation Team - Research → Writing → Editing → Publishing
Data Analysis Team - Collection → Analysis → Visualization → Reporting
Lead Qualification Team - Scoring → Research → Outreach → Follow-up
Research Team - Planning → Gathering → Synthesis → Reporting
Code Review Team - Linting → Review → Testing → Approval
Sales Team - Lead Gen → Qualification → Proposal → Close
Best Practices
Agent Specialization:
Give each agent a clear, focused role
Avoid overlap between agents
Optimize for specific tasks
Keep system prompts specialized
Team Design:
Start simple, add complexity as needed
Use parallel processing where possible
Implement error handling
Monitor agent performance
Routing Strategy:
Use routers for complex decision making
Cache routing decisions when possible
Provide clear routing criteria
Test edge cases
Performance:
Minimize agent handoffs
Use parallel execution
Cache common operations
Monitor credit usage
Configuration Examples
Simple Sequential Team
Input → Analyzer Agent → Writer Agent → Editor Agent → OutputAnalyzer: Understands the task
Writer: Creates content
Editor: Refines and polishes
Complex Customer Service Team
→ Technical Support Agent →
\
Input → Router → → Billing Agent → → Supervisor → Output
/
→ General Support Agent →Router: Analyzes inquiry type
Specialists: Handle specific domains
Supervisor: Ensures quality and completeness
Research & Analysis Team
→ Web Research →
\
Input → Planner → → Database Search → → Synthesizer → Reporter → Output
/
→ Expert Interview →Parallel research, combined synthesis, final reporting.
Visual Builder Features
Interface Components
Canvas - Drag-and-drop agent placement
Node Panel - Available node types
Connection Tool - Draw connections between agents
Zoom & Pan - Navigate large teams
Minimap - Overview of team structure
Node Configuration
Agent system prompts
Model selection
Tool assignments
Input/output mapping
Error handling
Testing & Debugging
Step-through execution
Agent output inspection
Connection tracing
Error visualization
Performance metrics
Monitoring & Analytics
Track your Workforce performance:
Execution Metrics - Success rates, timing, bottlenecks
Agent Performance - Individual agent stats
Cost Analysis - Credit consumption per agent
Error Tracking - Failure points and patterns
Quality Metrics - Output quality over time
Workforce vs Traditional Automation
Multiple AI Agents
✅ Yes
❌ No
❌ Single
Dynamic Routing
✅ Yes
⚠️ Limited
❌ No
Parallel Execution
✅ Yes
⚠️ Limited
❌ No
Visual Design
✅ React Flow
✅ Drag-drop
✅ Config UI
Agent Collaboration
✅ Yes
❌ No
❌ Solo
Conversational
⚠️ Optional
❌ No
✅ Yes
Complexity
🔴 High
🟡 Medium
🟢 Low
Power
🔴 Maximum
🟡 Medium
🟢 Basic
Troubleshooting
Common issues and solutions:
Agent loops: Check routing logic and exit conditions
Slow execution: Review parallel opportunities, optimize agents
Inconsistent results: Review agent prompts and coordination
High costs: Optimize agent calls, reduce redundancy
Complex debugging: Use step-through execution, inspect nodes
See Troubleshooting Guide for more help.
Real-World Use Cases
Customer Support Automation
Route inquiries to specialized agents (technical, billing, general), escalate complex issues to human supervisors.
Content Production Pipeline
Research team gathers information → Writing team creates content → Editing team refines → Publishing team distributes.
Data Analysis & Reporting
Multiple agents analyze different data sources in parallel, synthesis agent combines insights, reporting agent creates visualizations.
Lead Processing & Sales
Qualification agent scores leads → Research agent gathers context → Outreach agent personalizes communication → Follow-up agent manages pipeline.
See Use Cases for more examples.
Related Documentation
Agents - Individual agent configuration
Workflows - Sequential automation
Integrations - Tools for agents
API Reference - Workforce API
Learn More
Multi-Agent Systems Guide - Deep dive
AgenticFlow 101 - Foundation course
Video Tutorials - Visual learning
Community - Ask questions
Ready to build your first multi-agent team? Start the 15-Minute Quickstart →
Last updated
Was this helpful?