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Deploy agents to and monitor their performance through conversation logs, result categorization, and feedback tracking. Before going live, validate rules, tools, and forms in Testing Mode — a sandboxed chat that runs the agent end-to-end without writing tickets or sending messages.

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Testing Mode V3+

Testing Mode is a sandboxed chat panel for trying your agent without touching production data. The agent runs end-to-end — matching rules, calling tools, and choosing responses — but any action that would write or change something is simulated. No tickets are created and no messages are sent to Slack or Teams.Use Testing Mode to:
  • Validate a new before exposing it to real users.
  • Check that the right is presented for a given request.
  • Confirm sources are being cited correctly.
  • Inspect which tools the agent called and what it passed to them.

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Open Testing Mode

1

Open the agent

Navigate to Agents in the sidebar and select the agent you want to test.
2

Start a chat

Use the chat panel on the agent page. A yellow Testing Mode banner confirms the session is sandboxed.
3

Send a message

Type a request a real user might send, then submit. The agent responds with the same rules, tools, and knowledge it would use in production.

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Forms and submissions

When the agent presents a form, fill it out and submit it from within the chat. The values flow back to the agent so it can continue the turn, just like in production. Form submissions and ticket publishes appear as inline cards so you can verify what would have been created.After a terminal action (publishing a ticket or submitting a form), the session is frozen to mirror real-world behavior. Click Start new chat to reset and continue testing.

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Debug each turn

Every agent response includes a collapsible Debug disclosure below the message. Expand it to inspect:
  • Matched rules — which rules fired for the turn and the reasoning behind the match.
  • Tool calls — every tool the agent invoked, with the input it sent and the output it received.
  • Response type — the classification the agent chose (KB answer, Chat, Asked for info, or Escalated).
Use the debug view to diagnose unexpected behavior — for example, the wrong rule matching, a tool being called with bad inputs, or the agent escalating when it had enough knowledge to answer.
Testing Mode sessions are client-owned and not saved to Chat Logs. To capture a regression for later review, copy the relevant inputs and outputs from the Debug disclosure before resetting the session.

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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.

Answered

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 prompted the user to fill in a specific field on a form. This occurs while the agent is collecting structured information one field at a time during a form-based request.
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.

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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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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 the agent returned, such as Answered, Clarification, Ticket Created, Missing Knowledge, or Error. Use this to isolate conversations by outcome — for example, view every “Missing Knowledge” result to find knowledge gaps, or every “Error” result to investigate technical issues.
Filter conversations by the properties of the 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 )
  • Priority — filter by ticket
  • Assignee, Requester, Followers — filter by people on the ticket
  • Channel — filter by the the ticket lives in
  • Form — filter by the used to create the ticket
  • Tags, Category, Ticket Type — filter by ticket organization attributes
  • Custom fields — filter by any 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 Created 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 = Answered instead.

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Explain a conversation with Copilot

Use Explain to ask why the agent answered, asked, or escalated the way it did. Copilot inspects the run evidence — the response classification, the matched and its reasoning, the tools the agent called, the agent’s configuration, and any cited documents — and gives a grounded explanation rather than a guess.Open Explain in two ways:
  • Explain the whole conversation: open a chat log and select the Explain button at the top of the panel. Copilot walks through every turn and the overall outcome.
  • Explain a single response: hover an agent reply in the chat log and select Explain on that message. Copilot scopes the explanation to just that turn.
What Copilot covers on the first reply:
  • Outcome — the overall classification and the single reason the conversation landed there.
  • What happened, turn by turn — one bullet per agent reply, with the classification and a short description of what the agent did.
  • Verification — for Missing Knowledge or escalations, Copilot re-runs a knowledge search to check whether the gap is a real content gap or a retrieval miss. It also notes when a matched rule narrowed the agent’s knowledge lookup to specific documents or folders, which can make an escalation correct even though relevant content exists elsewhere.
  • Takeaway — what this means for you, such as whether the escalation was justified or whether a rule or knowledge gap may need attention.
Follow-up questions stay conversational and answer only what you ask. Copilot reuses the run context it already loaded instead of restating the full breakdown.
Explain is read-only — it analyzes and recommends, but it cannot edit agents, rules, or knowledge from inside the chat. When you ask Copilot to make a change (for example “update this rule” or “fix the escalation instructions”), it surfaces a one-click button that carries the conversation into the full Copilot panel, which has the tools to apply the change.

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Analyze conversations

Use chat logs to:

Identify knowledge gaps

Review conversations with “Missing Knowledge” 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 “Answered” results)
  • Maximum escalation rate (percentage of “Ticket Created” results)
  • Target feedback rate (percentage of responses receiving feedback)
  • Minimum positive feedback ratio (positive reactions vs negative reactions)
Last modified on July 27, 2026