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Deploy agents to and monitor their performance through conversation logs, result categorization, and feedback tracking.

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Connections

Select which channels the agent should be deployed to. You can add or remove channels at any time after deployment.

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Deploy agents

1

Navigate to agent settings

Open your agent’s settings page.
2

Find connections section

Locate the Connections section.
3

Add channels

Click Add Channel and select the channels where the agent should be active.
4

Save changes

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

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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.

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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

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Agent results

Agent results categorize how the agent ended each conversation. This helps you understand agent behavior patterns and identify areas for improvement.

Answer response

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 or escalation instruction explicitly directs the agent to show a . Forms are only shown when rules specifically require them, not based on the agent’s judgment about structured information needs.
The agent triggered a . This happens when the agent executes an automated process based on the conversation context.
The agent detected and handled sensitive information appropriately. This indicates the agent recognized confidential data and took appropriate security measures.
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.

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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.

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Analyze conversations

Use chat logs to:

Identify knowledge gaps

Review conversations with “Not found response” results to discover missing documentation. Add these topics to your to improve future responses.
Analyze conversations where the agent asked for clarification or created tickets unnecessarily. Update 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.

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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.
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)