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The AI Trigger node uses artificial intelligence to evaluate complex trigger conditions described in natural language. Instead of writing code or configuring rules, simply describe when you want the workflow to start.
AI Trigger node configuration form

AI Trigger configuration — describe the watch condition in plain language; the AI evaluates each incoming event.

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When to Use

  • Complex conditions - Triggers that are hard to express as simple rules
  • Content-based triggers - Start workflows based on the meaning or sentiment of incoming data
  • Intent detection - Trigger when messages match certain intents
  • Quality thresholds - Start processing when content meets certain criteria

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Writing Effective Conditions

The AI evaluates your condition against incoming data. Write conditions that are: Specific and unambiguous:
Focused on observable criteria:
Clear about what to include/exclude:

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Example: Urgent Email Router

Automatically escalate emails that need immediate attention:
1

Configure the AI condition

Add an AI Trigger node with:
  • Data source: Gmail channel
  • Condition:
2

Escalate the email

Connect to an External API (Slack) node to post to your #urgent-support channel:
3

Assign to on-call

Add another External API node to create a high-priority ticket in your support system.

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Example: Lead Quality Filter

Only process high-quality leads from form submissions:
Workflow:

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Example: Content Moderation

Trigger review for potentially problematic content:

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How It Works

  1. Data arrives from the configured source (email, message, webhook, etc.)
  2. The AI model evaluates the data against your condition
  3. If the condition is met (confidence above threshold), the workflow triggers
  4. The original data is passed through as eventData
AI evaluation typically takes 1-3 seconds. For high-volume triggers, consider using rule-based conditions (Condition node) for simple criteria.

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Confidence Threshold

By default, the event triggers when the AI is reasonably confident the condition is met. You can access the confidence score to add additional filtering:
This ensures only high-confidence matches proceed.

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Tips

Include examples in your condition to help the AI understand edge cases: “Examples of urgent: ‘System is down’, ‘Can’t access my account for 2 hours’. Not urgent: ‘When will feature X be available?’”
Use the matchReason output to log why emails/messages were flagged. This helps you refine the condition over time.
AI evaluation adds latency and cost compared to rule-based triggers. Use this node when the condition genuinely requires natural language understanding.

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Combining with Other Events

Use AI Triggers alongside other triggers:
  • AI Trigger for complex, nuanced conditions
  • Condition node (after Event from App) for simple rule-based filtering
  • Scheduled Trigger for scheduled AI analysis of accumulated data

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Settings

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string
default:"AI Trigger"
Display name shown on the canvas.
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string
default:"ai_custom_event_1"
Unique identifier for referencing outputs.
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string
required
Natural language description of when the event should trigger. Be specific and clear about the criteria.
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string
required
Where to look for data to evaluate:
  • channel - Monitor a connected app channel (Gmail, Slack, etc.)
  • variable - Monitor a workflow variable
  • webhook - Evaluate incoming webhook data
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object
Configuration specific to the data source (channel ID, variable name, etc.).

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Outputs

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object
The data that triggered the event, structured based on the source.
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number
AI confidence score (0-1) that the condition was met.
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string
Brief explanation of why the AI determined the condition was met.

App Trigger

Get raw events from apps, then filter with Condition nodes.

Condition

Add rule-based filtering after any trigger.