Let your AI agent take action

When your AI agent is helping in a conversation, it can do more than just write a reply. You can give it actions: things it can do in Chatlane (e.g. update the conversation status, add or remove tags, update the contact's details) or in other systems (e.g. create a ticket in your help desk, add a lead to your CRM). This article explains what agent actions are, how to set them up, and why they help support teams and managers.

What agent actions are

An action is something the agent can do during a conversation. The agent decides when to use an action based on what the customer said or what you asked it to do. When it uses an action, Chatlane runs it (e.g. updates the conversation status or sends a request to your CRM) and feeds the result back to the agent so it can respond naturally—for example, "I've marked this conversation as closed and added a note in your account."

Every action the agent takes is recorded so you can review what happened and improve how the agent is configured. That helps managers and founders see what the agent is doing and tune behaviour over time.

Actions inside Chatlane

You can let the agent do things inside Chatlane without connecting to any other system:

• Update conversation status — Mark the conversation as active, pending, or closed. Useful when the agent resolves an issue or hands off to a human.
• Add or remove tags — Add a tag to the conversation (e.g. "billing," "urgent," "resolved") or remove one. Useful for categorising and filtering conversations.
• Update contact details — Update the contact's name, email, or phone (e.g. when the customer corrects their information in the thread).

You turn these on when you set up the agent; there's no coding. For tags, you can optionally limit which tags the agent is allowed to use (e.g. only "billing," "urgent," "resolved") so it doesn't create unexpected tags.

Actions in your own systems (webhooks)

You can connect the agent to your own tools or APIs. When the agent decides it should do something (e.g. create a ticket in your help desk or update a record in your CRM), Chatlane sends a request to a URL you provide. You choose the method (e.g. GET or POST), headers, and body (for POST). You can use placeholders so the request includes the contact, conversation, or other details—for example, the contact's email or the conversation subject.

This is useful for:

• CRMs — Create or update a lead or contact when the agent identifies a sales opportunity or updates contact info.
• Ticketing systems — Create a ticket or update its status when the agent escalates or resolves an issue.
• Internal dashboards or tools — Notify another system when the agent takes a certain action.

You don't need to write code in Chatlane; you configure the URL, method, and placeholders in the agent's action settings.

Actions via Zapier

If you use Zapier, you can connect the agent to thousands of apps (e.g. Gmail, Slack, Trello, Salesforce) without writing URLs or code. You sign in to Zapier once, then in Chatlane you pick the app and action (e.g. "Create task in Trello" or "Send email via Gmail"). You map the agent's inputs to the action's fields (e.g. "conversation subject" → "task name"). When the agent calls that action, Chatlane runs it through Zapier and returns the result to the agent so it can confirm to the customer.

This is useful when you already use Zapier for other automations and want the agent to trigger the same apps (e.g. create a Slack message, add a row to a spreadsheet, or send an email).

Why this helps support teams and managers

• Fewer manual steps — The agent can close conversations, tag by topic, or update contacts as it goes, so your team spends less time on repetitive clicks.
• Consistent tagging and status — Managers get predictable tags and status updates when the agent is configured with clear rules and allowed tags.
• Support connected to the rest of the business — Founders and managers can connect support to CRM or product tools without developer time; webhooks and Zapier handle the integration.
• Audit trail — Every action the agent takes is recorded so you can review and improve behaviour and quality.

For giving your agent a knowledge base (files and message attachments), see Give your AI agent a knowledge base. For turning on self-learning so the agent improves from real conversations, see Let your AI agent learn from your best conversations.