# Agenticflow Workforce > Deploy and operate a multi-agent AgenticFlow workforce — a DAG of agents that hand off to each other (trigger → coordinator → worker agents → output). Use when the user asks for a team, pipeline, or multi-agent system: research-then-write, triage-then-specialist, dev shop, marketing agency, sales team, content studio, support center, Amazon seller team. Choose this skill over agenticflow-agent when the ask mentions 'team', 'workforce', 'pipeline', 'multiple agents', 'delegation', 'handoff', or names a built-in blueprint. Provides the `af workforce *` command surface, blueprint decisions, graph wiring, MCP attach recipes, and public URL publishing. - Skill: `antongulin/agenticflow-workforce-2` (Agent Skill) - Install (CLI): `npx skillmds@latest add antongulin/agenticflow-workforce-2` - Raw SKILL.md: https://api.skillmd.com/api/skills/antongulin/agenticflow-workforce-2/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Marketing & Growth - Author: antongulin (https://skillmd.com/u/antongulin) - Updated: 2026-09-22 - Page: https://skillmd.com/skills/antongulin/agenticflow-workforce-2 --- # AgenticFlow Workforce A workforce is an AgenticFlow-native **DAG of agents** — typically `trigger → coordinator agent → worker agents → output` — that hand off structured results to each other. Use this when orchestration between roles matters. ## When NOT to use this skill If the user wants a **single chat endpoint, a customer-facing bot, one assistant, or routing-by-prompt inside one agent**, use `agenticflow-agent` instead. A single-agent solution with rules in the system prompt is simpler, cheaper, and easier to iterate on. Workforces are for genuine multi-role orchestration. ## Orient first ```bash af bootstrap --json ``` Returns `auth`, `agents`, `workforces`, `blueprints`, `commands`, `playbooks`, `whats_new`, `_links`. Extract: - `auth.project_id` — required for agent creation (the agents inside your workforce) - `auth.workspace_id` - `_links.workspace` — **surface this URL to the user right away**: *"Your workspace is at `<_links.workspace>` — open it anytime to see the workforce I'm building."* The user needs a human-first anchor before the first mutation - `blueprints[]` — the built-in team templates, each with required/optional slot counts - `workforces[]` — any existing workforces in the workspace (empty initially is normal; check `data_fresh` if false — that means the backend was unreachable, not the workspace empty) If `data_fresh: false` in the bootstrap response, the backend is degraded — **do not mutate**. Run `af doctor --json --strict` and fix auth/network before proceeding. ## The built-in blueprints | Blueprint | Required slots | Optional | | --- | --- | --- | | `dev-shop` | ceo, engineer | designer, qa | | `marketing-agency` | ceo, cmo, designer | researcher | | `sales-team` | ceo, researcher, general | — | | `content-studio` | ceo, cmo, engineer | designer | | `support-center` | ceo, general | researcher | | `amazon-seller` | ceo, cmo, engineer, researcher | general | | `tutor` | ceo, cmo, engineer, researcher | general | | `freelancer` | ceo, cmo, engineer, researcher | general | ## One-command deploy (v1.6+) Always preview with `--dry-run` first: ```bash af workforce init --blueprint --name "" --dry-run --json af workforce init --blueprint --name "" --json ``` `init` creates the workforce + one real agent per required slot + the wired graph — in a single atomic call. On failure, every resource is rolled back automatically; inspect `details.rolled_back_agents` and `details.rolled_back_workforce` in the error. Use `--include-optional-slots` to fill every slot, not just required ones. Use `--model ` (e.g. `agenticflow/gemini-2.0-flash`) to override the default model for all auto-created agents. ## Custom workforce (no blueprint fits) If the user's ask is a precise custom pipeline that no blueprint matches (e.g. a 2-step researcher → writer flow that doesn't fit the 3–5-agent blueprints), skip blueprints: 1. Inspect the expected graph shape: ```bash af schema workforce --field schema --json ``` 2. Create metadata only: ```bash af workforce create --body '{"name":"Raul Content Pipeline","description":"..."}' --json ``` 3. Create each agent separately via `af agent create` (see `agenticflow-agent` skill). 4. Build a graph JSON with `trigger → researcher (agent node, agent_id from step 3) → writer (agent node) → output`. 5. Deploy the graph atomically: ```bash af workforce deploy --workforce-id --body @graph.json --json ``` 6. Validate: ```bash af workforce validate --workforce-id --json ``` Edge `connection_type` is one of `next_step`, `condition`, `ai_condition`. Agent nodes **require** a real `agent_id` in `input`. ## Run + publish ```bash af workforce run --workforce-id --trigger-data '{"message":"..."}' # Streams SSE events — each line is one JSON event. The CLI auto-wraps your # payload in {trigger_data: ...} — don't wrap it yourself. af workforce publish --workforce-id --json # Mints a public_key + public_url — hand this to teammates so they can run the # workforce without platform auth. af workforce versions publish --workforce-id --version-id --json # Snapshot + publish a specific version (draft/published/restore workflow). ``` ## Attach MCP tools per agent (not per workforce) MCP clients attach to individual agents, not to the workforce graph. After `init`, use: ```bash af mcp-clients list --name-contains "google sheets" --fields id,name --json af mcp-clients inspect --id --json # Check pattern before attach af agent update --agent-id --patch --body '{"mcp_clients":[{"mcp_client_id":"","run_behavior":"auto_run","tools":{}}]}' --json ``` See the `agenticflow-mcp` skill for the Pipedream vs Composio write-safety distinction. ## Cleanup Workforces + agents are billed while they exist. Delete test deploys: ```bash af workforce delete --workforce-id --json # For each auto-created agent id captured from init: af agent delete --agent-id --json ``` Both return `{"schema":"agenticflow.delete.v1","deleted":true,"id":"...","resource":"..."}` on success. ## On errors All API errors return `{schema: "agenticflow.error.v1", code, message, hint, details}`. Read `hint` first — it points at the recovery command (e.g. `af list` on a 404). For 422s, inspect `details.payload.detail` for field-level errors. `workforce run` occasionally returns a backend `Failed to retrieve user info for user_id: api_key:...` 400 — this is a known server-side issue with API-key auth, not a CLI bug.