Lookups, actions, and AI nodes
The nodes that do work: look up customer context, take actions like creating a ticket, and run AI steps that degrade honestly.
Beyond talking to the customer, a flow can look things up, take real actions, and call on AI. These are the nodes that let a flow resolve an issue rather than just route it.
Lookup nodes
A lookup fetches context and branches on whether it found anything (a found path and a not-found path). The available lookups are:
- Identify customer: resolve who the visitor is.
- Open conversations: find the visitor's existing open conversations, so the flow can offer to continue one instead of starting fresh.
- Recent orders (placeholder): a stub for order lookups, ready for a future data source.
A lookup can save its result under a variable for later nodes to use.
Action nodes
An action performs a real change and branches on success or failure. The actions are:
| Action | What it does |
|---|---|
| Create ticket | Opens a support ticket; saves the ticket id. |
| Create task | Creates a work item; saves the task key. |
| Add tag | Applies a tag to the conversation. |
| Set priority | Sets the conversation's priority. |
| GitHub: create issue | Opens a GitHub issue; saves the issue URL. |
| GitHub: comment | Comments on a linked GitHub issue. |
| Slack: notify | Sends a Slack notification. |
| Continue by email | Moves the conversation to email so it can carry on asynchronously. |
Action parameters accept variable substitution, so a created ticket can carry the subject the customer typed earlier.
AI nodes
An AI node runs a language-model step. There is one node type with a kind for each job:
- Classify intent: label what the customer wants.
- Search knowledge: pull relevant context from your public corpus (help articles, feedback, changelog).
- Generate answer: draft a reply, optionally grounded in your knowledge.
- Summarize: condense the conversation so far.
- Detect sentiment and detect urgency: read the emotional tone or urgency.
- Extract: pull structured fields out of free text.
Every AI node routes three ways: done (a usable result at or above a confidence threshold you set), low confidence (a result below the threshold), and failure (the model was unavailable or errored). This is the honest-degrade principle in action: when the AI cannot help, the flow takes the failure branch and hands off to a human rather than inventing an answer. Routing on an AI result stays composable: a classify-intent node saves its label to a variable, and a plain condition node branches on it.