Workflow patterns

These patterns combine step types, Command Center, and integrations into workflows you can adapt for production. See the JSON cookbook for full graph examples.

Approve then send

Pause before any outbound action that affects customers or production data.

Graph structure

Draft step — llm_call or mcp_call generates content (email body, WhatsApp message, CRM update payload)
human_task — task_type: "approval" with draft in task_payload
Send step — mcp_call to gmail (send_email), whatsapp (send_message), or another connector — depends_on the human task, uses approved payload

Example human task payload

{
  "task_type": "approval",
  "task_payload": {
    "title": "Send order confirmation",
    "description": "Review before sending to {{input.customer_email}}",
    "draft_subject": "{{steps.draft-email.result.subject}}",
    "draft_body": "{{steps.draft-email.result.body}}"
  }
}

Complete approvals from Command Center. Downstream steps read the completion result.

Do not skip the human task for regulated or high-risk outbound messaging. A rejected task stops the branch — design downstream steps to handle rejection gracefully or use condition to branch.

Inbound webhook triggers

Start workflows from external systems (CRM, payment processor, monitoring alert).

Setup

Create a workflow whose first step validates {{input.*}} fields
Create an inbound webhook subscription — see Inbound webhooks
Configure the external system to POST to the subscription URL with the signing secret
Map POST body fields to workflow input

Validate-first pattern

Always validate required input before calling external tools:

{
  "id": "validate-input",
  "type": "lua_script",
  "name": "Validate webhook payload",
  "script": "if not input.order_id then error('order_id required') end\nreturn { order_id = input.order_id }",
  "timeout_s": 5
}

Or use condition on downstream steps: "condition": "{{input.order_id}}"

Idempotency

External systems may retry webhooks. Design workflows to be idempotent:

Check if the order/event was already processed (MCP query step)
Use external IDs in tool_args so duplicate runs do not double-send

AgentRuntime does not provide built-in cron scheduling today. For recurring runs, use an external scheduler that calls the API or fires an inbound webhook on a timetable.

Error handling with retries

Per-step retries

Set retry_count on steps that call flaky external APIs:

{
  "id": "fetch-data",
  "type": "mcp_call",
  "name": "Fetch from API",
  "tool_name": "postgres_query",
  "retry_count": 3,
  "timeout_s": 30
}

The runtime retries after failure up to retry_count times before marking the step failed.

Timeouts

Set timeout_s on every external call. Defaults are not unlimited — long-running MCP or LLM calls without timeouts can stall runs.

Step type	Suggested starting timeout
MCP tool call	30–120s depending on API
LLM call	60–180s for long generations
Lua script	5–30s (hard cap 30s)
for_each	Sum of body timeouts × parallelism

Failure propagation

When a step fails after retries:

The run marks the step failed
Downstream steps that depends_on the failed step are skipped
The run appears in Command Center Failed recently

Recover by fixing the root cause (connection, credits, tool args), publishing a new workflow version if needed, and starting a new run.

Human escalation on failure

Route low-confidence or failed LLM output to human review instead of failing silently: Use condition or a Lua branch to set a flag, then add a human_task on the low-confidence path.

Parallel fan-out

Use for_each to process a list with bounded parallelism:

{
  "id": "notify-each",
  "type": "for_each",
  "for_each_items": "{{steps.fetch-recipients.result.contacts}}",
  "for_each_max_parallel": 5,
  "for_each_body_steps": [
    {
      "id": "send",
      "type": "mcp_call",
      "tool_name": "send_message",
      "tool_args": {
        "to": "{{foreach.item.phone}}",
        "body": "{{steps.build-template.result.text}}"
      },
      "timeout_s": 30
    }
  ]
}

Keep for_each_max_parallel low for rate-limited APIs (WhatsApp, email providers).

Memory-enriched steps

When memory is enabled, workflow prepare-step can inject long-term context before LLM steps. See Memory (preview). Pattern:

Index conversations or files into memory
LLM step prompt references memory search results passed via prepare-step context
Human task approves actions informed by retrieved knowledge

Pattern checklist

Pattern	Human task?	Retries?	Trigger
Approve then send	Yes	On draft step optional	Manual / API
Webhook automation	Optional	On MCP steps	Inbound webhook
Scheduled report	Optional	On extract steps	External cron → API
Bulk processing	Optional	On for_each body	API / webhook

End-to-end guides

These patterns are expanded into full step-by-step recipes in Guides:

See all guides.

Command Center — approve tasks and monitor runs
Human tasks — task authoring reference
Inbound webhooks — external triggers
Connector catalog — common patterns
Troubleshooting — recover from failures

Get started

Platform

Workflows

Guides

Integration setup

Connectors

AI & chat

API

Approve then send

Graph structure

Example human task payload

Inbound webhook triggers

Setup

Validate-first pattern

Idempotency

Error handling with retries

Per-step retries

Timeouts

Failure propagation

Human escalation on failure

Parallel fan-out

Memory-enriched steps

Pattern checklist

End-to-end guides

​Approve then send

​Graph structure

​Example human task payload

​Inbound webhook triggers

​Setup

​Validate-first pattern

​Idempotency

​Error handling with retries

​Per-step retries

​Timeouts

​Failure propagation

​Human escalation on failure

​Parallel fan-out

​Memory-enriched steps

​Pattern checklist

​End-to-end guides

​Related docs

Approve then send

Graph structure

Example human task payload

Inbound webhook triggers

Setup

Validate-first pattern

Idempotency

Error handling with retries

Per-step retries

Timeouts

Failure propagation

Human escalation on failure

Parallel fan-out

Memory-enriched steps

Pattern checklist

End-to-end guides

Related docs