Using AI Agent Actions

While AI Agent is replying to a Contact, it can perform specific platform actions like assigning the conversation, closing it, or updating contact information. Understanding these actions helps you design AI Agents that handle conversations smoothly and logically.

This guide covers exactly how each action works, how to write effective instructions for them, and how to avoid common pitfalls like conflicts with your existing Workflows.

Why Actions Are Important

Your AI Agent’s replies help handle customer conversations smoothly. But often, replying alone isn’t enough—your Agent also needs to take clear next steps to move Leads across sales or support pipeline.

That’s exactly what Actions are for. They help your AI Agent move beyond just replying, ensuring conversations progress logically down the conversion funnel, customer details stay accurate, and teams always know who should handle each chat next.

By using the right actions, your AI Agent knows exactly what to do while replying—giving your team peace of mind and keeping your conversations organized and effective.

Actions Your AI Agent Can Take

In this guide, you’ll learn how to configure them by prompting and directing your AI Agent. The key to agentic behavior lies in providing the AI Agent with enough context—clearly defining what to do, and when to do it.

During a conversation, your AI Agent can take the following actions:

Close conversations

What this action does:

This action can be used to close a conversation once the Contact’s request has been fully resolved. Think of it as your AI Agent politely wrapping up a chat in person.

• You can also provide a closing note—a label that explains why the conversation ended. To do this, just mention the closing note category in your instructions. For example:
  

  - If the contact has no more questions, close the conversation and select closing note "Issue Resolved"

• When AI Agent closes a conversation, it will also automatically summarize the conversation and select a closing note.

  Note: If you don’t want summaries or notes, you can instruct: “Do not generate a summary or choose a closing note.” For example:
  

  - If the contact has no more questions, close the conversation and do not generate a summary nor choose a closing note. 

When to use:

• Use this action when the AI Agent has completed the customer’s request—like answering FAQs, collecting feedback, or solving a simple issue—so conversations don’t stay open unnecessarily.

Best practices:

• Always include the exact instruction “Close the conversation” so AI Agent knows to trigger it.

• Clearly explain the specific scenario where the conversation should close (e.g., “If the customer confirms they have no more questions…”).

• Pair this action with a friendly, definitive closing response for AI Agent to use such as: “I hope that helps! If you have other questions, feel free to message again.”

Limitations:

• AI Agents can only choose from existing closing notes. They cannot create new closing notes or modify your existing list. To ensure the correct closing note is applied, use the exact closing note name in your instructions.

• AI Agents can only summarize the latest 20 messages in a conversation. Older messages will not be included in the summary. If important context appears earlier in the conversation, instruct the AI to ask a clarifying question or restate key details before closing.

Assign to agent or team

What this action does:

Assigns the right human agent, team, or even another AI Agent once the AI Agent has done its part. This is especially useful for escalating to a human when the AI Agent can’t resolve an issue, ensuring customers always get the right level of support.

You can also control how team members get assigned. There are two methods:

• Round Robin: Assigns conversations in rotation so workload is shared evenly.

• Least Open Conversation: Assigns to whoever currently has the fewest active conversations.

  Note: If no method is specified, AI Agent defaults to round robin.

- If the contact asked about their appointment schedule, assign to @Scheduling Team by round robin.

You can also assign to individual agents or even unassign a conversation by instructing, "Unassign the conversation".

When to use:

• Use this action when the conversation needs to move beyond the AI Agent’s scope—for example, to a human agent for deeper support, to a specialized team (like Billing or Sales), or even to another AI Agent designed for a different task.

Best practices:

• Use these terms in the actions instruction: “Assign to @User/Team Name” to trigger this action for a scenario. For example, “If {something happened}, assign to @User/Team Name”.

• Ensure your AI Agent asks relevant qualification questions to determine the right team.

• Clearly define conditions in your instructions (e.g., “Assign to Sales if customer mentions pricing or demo”).

Limitations:

• AI Agents cannot see who is online. They have no visibility into agent availability or presence, so they cannot choose the “next available” or “currently active” user. Always specify the exact team or agent name if availability matters.

• AI Agents cannot schedule assignments for a future time. Instructions like “Assign to Sales at 5pm” will not work because the AI cannot delay an assignment. However, immediate assignment logic based on current conditions (e.g., “Assign to Sales if within business hours”) will work, as the AI evaluates the condition in real time and assigns immediately.

• AI Agents cannot run round robin across multiple teams. They can only distribute conversations within one specific team at a time.

• AI Agents do not choose the assignment method (round robin vs least open). However, round robin can only be applied within a team, not across individual users. This means agents must be part of a team for round robin to work. If you want to use least open or any other method, you must explicitly specify it in your instructions; otherwise, the AI will default to round robin at the team level.

Update Contact fields

What this action does:

Updates Contact details (name, phone number, budget, etc.) automatically based on information collected during the conversation. This is especially useful for keeping your Contact’s data up to date, so agents can focus on meaningful interactions instead of admin work.

• You can also mention Contact field names in your instructions to improve accuracy and ensure data is saved in the right place. For example:

- If the Contact provided their budget, save it as Budget field.

When to use:

• For lead qualification, customer onboarding, or booking appointments.

Best practices:

• Clearly provide AI Agent with instructions to ask the customer for specific details (e.g., “Could you share your preferred email address?”).

• Train your AI Agent with knowledge sources so it gives accurate, context-specific answers when replying to Contacts.

Limitations:

• AI Agents cannot detect when a Contact field update fails. If the field name is unclear, misspelled, or doesn’t exist, the update simply won’t happen, and the AI won’t know it failed. To avoid this, always use the exact field name or field ID in your instructions.

• AI Agents cannot create or delete Contact fields. They can only update fields that already exist in your workspace. If you need new fields or structural changes, create them manually before referencing them in your AI Agent instructions.

Update Lifecycle stage

What this action does:

Changes a customer’s Lifecycle stage (e.g., from “New Lead” to “Qualified Lead”) based on conversation context. This is especially useful for automating lead progression and keeping your sales pipeline accurate without manual updates.

When to use:

• When the AI Agent qualifies a lead or achieves a specific milestone in customer engagement.

Best practices:

• Clearly define the criteria needed to advance the Contact’s Lifecycle stage.

• Always provide the exact Lifecycle Stage name so the AI Agent updates to the right stage at the right time. For example:

- Once the customer confirms their interest in a demo, update the Lifecycle stage to Qualified.

Limitations:

• AI Agents cannot create new Lifecycle Stages. They can only update a Contact to stages that already exist in your workspace. If you want to use a new stage, you must create it first before referencing it in your instructions.

Writing Instructions to Trigger Actions

Good AI Agent instructions directly lead to clear, actionable responses. When writing instructions, keep these best practices in mind:

• Clearly specify conditions and desired outcomes. For example:

- If the customer asks about pricing, reply with our standard packages and assign the conversation to @Sales Team.

• Use straightforward, simple language. Avoid ambiguity.

• Anticipate common scenarios and build clear, simple instructions for each action.

• You can also define what to tell the customer when an action is used. For example:

- Once booking date and time are collected, reply to the Contact: "Thank you for your booking. We look forward to seeing you on $contact.bookingdate at $contact.bookingtime."

Timing & Action Behavior

• AI Agents always perform actions before generating a reply.

• Actions like “Close Conversation” or “Assign Conversation” take effect once a scenario is received. For example, if the Contact said there are no more questions, AI Agent will first close the conversation, and then generate a thank you note.

How actions work with audio messages

AI Agents can now understand audio messages shared by Contacts in real conversations.

When an audio file is received:

• It is automatically transcribed into text so the AI Agent can understand its content.

• The AI Agent processes the transcribed message and can perform relevant actions, such as assigning the conversation, updating Contact fields, or closing it, based on what was said.

• You can view transcripts of audio messages sent to AI Agents below the message bubble.

Important notes:

• Audio messages that are sent before a conversation is assigned to an AI Agent will not be taken into context and will not be displayed.

• Transcriptions may not show up instantly. Refresh your browser or switch to another conversation and back in to view transcript.

Supported audio and video file formats:

• Audio: MP3, WAV, FLAC, M4A, OGG/OGA, Opus, AAC, AIFF, WEBA

• Video: AVI, M4V, MKV, MOV, MP4, MPEG, MPG, WEBM, WMV, 3GPP

Chaining Actions Together

You can combine multiple actions in one scenario with common examples below.

For chained actions (For example, 1️⃣ Update Lifecycle → 2️⃣ Update Contact Field → 3️⃣ Close Conversation), make sure to place them in “Instructions” and ensure that the actions are turned on.

Examples:

Update Contact Field + Assign Conversation

- Collect and save Phone Number, then assign the conversation to a @Sales Team by round robin.

Update Lifecycle Stage + Close Conversation

- Update Lifecycle to "Issue Resolved", and close the converstion by summarising the customer issue and the solution provided.

Best practices:

• Keep it logical and goal-driven: Only chain actions if they naturally follow one another (e.g., qualify a lead → close the conversation → assign to Sales).

• Keep the order clear: Place actions in a sequence that reflects how a real conversation would flow — don’t assign a conversation before collecting the key info.

• Don’t overload one scenario: Stick to 2–3 actions max. Too many chained actions can make it harder for the AI Agent to predict and troubleshoot.

• Be explicit in your instructions: Clearly state each step, rather than relying on the AI Agent to “guess” what comes next.

What to avoid:

• Vague instructions

- If it feels like the issue is resolved, maybe update Lifecycle or something.

Problem: AI Agent won’t know which Lifecycle stage to use or when to trigger the action.

• Contradicting actions

- Assign to @Sales Team, then unassign if not needed.

Problem: This creates confusion for the AI Agent and leaves the conversation in an undefined state.

• Overloading one scenario

- Update Lifecycle to Qualified, collect phone number, update Contact Field with location, assign to @Sales Team, assign again to @Support Team, and close the conversation.

Problem: Too many steps in one chain make the flow unpredictable and hard to debug. Best to split them and follow natural order. For example: First, ask for phone number, then save it. Second, update Lifecycle to “Qualified", etc

Avoiding Workflow & Assignment Conflicts

When setting up actions, ensure they don’t conflict with your existing Workflows or manual team assignments:

• Check Workflow assignments: If you have Workflows assigning conversations automatically, ensure these don’t override your AI Agent’s assignments unintentionally.

• Default AI Agent considerations: If using a default AI Agent, ensure its actions align with any automated Workflows that trigger at conversation open.

Best practice: Regularly review your Workflows alongside AI Agent settings to avoid conflicts and unexpected behavior.

FAQ and Troubleshooting