*** Connections Select which channels the agent should be deployed to. You can add or remove channels at any time after deployment. Deploy agents Open your agent’s settings page. Locate the **Connections** section. Click **Add Channel** and select the channels where the agent should be active. Save your connection settings to deploy the agent. Agents can be deployed to multiple channels simultaneously. Each channel operates independently with the same agent configuration. Learn about [setting up and managing channels](/documentation/tickets/channels) *** Chat logs All interactions between users and the agent are logged and can be reviewed in the Chat Logs section. Monitor agent performance, review conversations, identify knowledge gaps, and track response quality. View conversations Access all conversations your agent participates in from the **Chat Logs** tab on the agent’s page. Each conversation shows: * **Timestamp**: When the conversation started * **Requester**: User who initiated the conversation * **Channel**: Where the conversation took place * **Agent result**: How the agent categorized the conversation outcome * **Message count**: Number of messages exchanged * **Feedback**: Thumbs up or down reactions received Agent results Agent results categorize how the agent ended each conversation. This helps you understand agent behavior patterns and identify areas for improvement. The agent successfully answered the user’s question using knowledge base articles or direct information. This indicates the agent found relevant documentation and provided a complete answer. The agent asked the user for clarification or additional information. This occurs when the initial request lacks sufficient detail for the agent to proceed. The agent created a ticket for the request. This happens when the agent escalates to human assistance or when the request requires a structured form submission. The agent provided help or guidance information. This typically occurs when users ask about the agent’s capabilities or how to use the system. The agent could not find relevant information to answer the question. This indicates a potential knowledge gap in your documentation or a request outside the agent’s scope. The agent engaged in conversational interaction without a specific categorizable outcome. This includes general conversation or exploratory questions. The agent initiated a form-based request process. This occurs when a rule or escalation instruction explicitly directs the agent to show a form. Forms are only shown when rules specifically require them, not based on the agent’s judgment about structured information needs. The agent triggered a workflow. This happens when the agent executes an automated process based on the conversation context. The agent detected sensitive information (passwords, API keys, tokens, or credentials) in its response and delivered it as a private DM to the requester instead of in the shared channel. Other participants see a placeholder message indicating a private message was sent. V3+ The agent encountered an error during processing. This indicates a technical issue that prevented the agent from completing its response. The agent created a custom ticket type. This occurs when the agent uses a specialized form or ticket template. The conversation was not published or remained in draft state. This typically happens when the agent prepared a response but it was not sent. Feedback tracking Track user reactions to agent responses through thumbs up and thumbs down feedback. This data helps identify knowledge gaps and improve agent effectiveness. **View feedback summary:** * Total positive reactions * Total negative reactions * Feedback rate (percentage of responses receiving feedback) * Trending issues (frequent negative feedback patterns) **Filter by feedback:** * View all conversations with positive feedback * View all conversations with negative feedback * View conversations without feedback When **Create ticket on negative feedback** is enabled in agent settings, thumbs down reactions automatically create support tickets for human follow-up. Filter chat logs Use the **Filters** button above the chat log list to narrow down conversations. Filters use AND logic, so a conversation must match every active filter to appear. Filter by the result type the agent returned, such as Answer, Ask user, Ticket, Not found, or Error. Use this to isolate conversations by outcome — for example, view every “Not found response” to find knowledge gaps, or every “Error response” to investigate technical issues. Filter conversations by the properties of the ticket the conversation produced. The **Tickets** folder groups all ticket-level filters in one place. Available filters include: * **Title** — filter by keywords in the ticket title * **Description** — filter by keywords in the ticket description * **Status** — filter by ticket status (open, in progress, resolved, closed, or any custom status) * **Priority** — filter by ticket priority * **Assignee**, **Requester**, **Followers** — filter by people on the ticket * **Channel** — filter by the channel the ticket lives in * **Form** — filter by the form used to create the ticket * **Tags**, **Category**, **Ticket Type** — filter by ticket organization attributes * **Custom fields** — filter by any custom field defined on your forms * **Created at**, **Updated at**, **Due date** — filter by date ranges The **Title** and **Description** filters perform substring matches against the ticket’s title and description text. Use them to locate conversations tied to tickets about a specific topic — for example, every chat log that produced a ticket with “VPN” in the title. Combine filters to investigate specific patterns. For example: * **Find unresolved escalations**: filter by **Agent Response** = `Ticket response` and **Tickets → Status** = `open` to surface every escalated ticket still waiting for a human. * **Audit a single team’s load**: filter by **Tickets → Channel** = `IT` to see only chat logs that produced tickets in the IT channel. * **Investigate a custom workflow**: filter by **Tickets → Form** = `Access Request` to review every conversation that created an access request. * **Search by topic**: filter by **Tickets → Title** contains `password reset` to find every chat log whose ticket title mentions password resets, or use **Tickets → Description** to match keywords inside the ticket body. Ticket filters apply only to conversations that produced a ticket. Conversations the agent answered directly from the knowledge base will not appear when a ticket-level filter is active. To review knowledge-only conversations, filter by **Agent Response** = `Answer response` instead. Analyze conversations Use chat logs to: Review conversations with “Not found response” results to discover missing documentation. Add these topics to your knowledge base to improve future responses. Analyze conversations where the agent asked for clarification or created tickets unnecessarily. Update rules to handle these scenarios more effectively. Track how often the agent escalates to human assistance. High escalation rates may indicate unclear instructions or insufficient knowledge base coverage. Compare agent results over time to measure improvement. Track metrics like answer rate, escalation rate, and feedback trends. After updating agent configuration, rules, or knowledge base content, review chat logs to confirm improvements in agent responses. *** Best practices Check chat logs weekly to stay informed about agent performance and identify emerging issues early. Review negative feedback promptly to understand user frustrations and address gaps in agent capabilities or knowledge base content. Monitor agent result distribution over time. Sudden changes in result patterns may indicate issues with agent configuration, knowledge base updates, or changing user needs. Use chat log insights to continuously improve agent configuration, rules, escalation instructions, and knowledge base content. Establish target metrics for agent performance: * Minimum answer rate (percentage of “Answer response” results) * Maximum escalation rate (percentage of “Ticket response” results) * Target feedback rate (percentage of responses receiving feedback) * Minimum positive feedback ratio (positive reactions vs negative reactions)

Documentation Index