| name | managing-relevance-workforces |
| description | Manages Relevance AI workforces (multi-agent systems) - creating workflows, configuring nodes/edges, triggering execution, and debugging runs. Use when building multi-agent pipelines, connecting agents together, or debugging workforce execution. |
Managing Relevance AI Workforces
Skill for creating, configuring, triggering, and debugging Relevance AI workforces (multi-agent systems).
📚 Full API Documentation: If MCP tools don't cover your use case, see https://api-{region}.stack.tryrelevance.com/latest/documentation (replace {region} with your project's region)
✅ RECOMMENDED APPROACH: Workforces are the official way to build multi-agent systems.
The legacy pattern of adding sub-agents to an agent's actions array is DEPRECATED.
Always use workforces for agent-to-agent orchestration.
When to Use
- Creating multi-agent workflows (pipelines)
- Connecting agents together with edges
- Configuring handovers between agents
- Triggering workforce execution
- Debugging workforce runs (seeing what each agent produced)
MCP Tools
| Tool | Description |
|---|
relevance_list_workforces | List all workforces |
relevance_get_workforce | Get full workforce config with nodes/edges |
relevance_create_workforce | Create new workforce with agents |
relevance_update_workforce | Update workforce graph/metadata |
relevance_delete_workforce | Delete a workforce |
relevance_trigger_workforce | Trigger workforce with a message |
relevance_get_workforce_task_messages | Get execution details and agent outputs |
Quick Start: Create Workforce
relevance_create_workforce({
name: 'Research Pipeline',
description: 'Research -> Summarize -> Report',
agents: [
{ agentId: 'researcher-agent-id' },
{ agentId: 'summarizer-agent-id' },
{ agentId: 'reporter-agent-id' },
],
});
const { workforce_task_id } = await relevance_trigger_workforce({
workforceId: 'workforce-id',
message: 'Research AI trends in 2024',
});
const execution = await relevance_get_workforce_task_messages({
workforceId: 'workforce-id',
taskId: workforce_task_id,
});
Testing: Define Before Building
IMPORTANT: Before building any workforce, create a testing rubric and get user approval.
Rubric Template
Testing Rubric for "[Workforce Name]":
□ End-to-End Flow
- [Trigger → Final output works with typical input]
- [All agents in chain execute successfully]
□ Agent Handovers
- [Data passes correctly between agents]
- [Each agent receives expected context]
□ Edge Cases
- [Handles agent failures gracefully]
- [Timeouts are handled appropriately]
□ Output Quality
- [Final output meets business requirements]
- [Intermediate outputs are logged/accessible]
Testing Workflow
- Present rubric to user before building
- Get approval or incorporate feedback
- Build the workforce
- Trigger with test input using
relevance_trigger_workforce
- Check execution using
relevance_get_workforce_task_messages
- Report results with pass/fail for each check
Example: Research Pipeline Rubric
Testing Rubric for "Lead Research Pipeline":
□ End-to-End Flow
- Given a LinkedIn URL, produces enriched lead report
- All 3 agents (Researcher → Enricher → Reporter) complete
□ Agent Handovers
- Researcher output includes profile data
- Enricher receives profile and adds company context
- Reporter receives all data and formats final output
□ Edge Cases
- Invalid LinkedIn URL produces clear error (not silent failure)
- Private profiles handled with partial data message
□ Output Quality
- Final report is structured and actionable
- Sources/data provenance is clear
Reference Files
URL Patterns
# Workforce edit page
https://app.relevanceai.com/workforces/{region}/{project}/{workforceId}
# Workforce task view
https://app.relevanceai.com/workforces/{region}/{project}/{workforceId}/tasks/{taskId}