> ## Documentation Index > Fetch the complete documentation index at: https://docs.ravenna.ai/llms.txt > Use this file to discover all available pages before exploring further. # Deploy & monitor > Deploy AI agents to Slack channels, test them in a sandboxed Testing Mode, then monitor conversations, results, and feedback to improve responses. Deploy agents to channels 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. ## Deployment model Agents are deployed to channels. Key behaviors: * An agent can be deployed to multiple channels simultaneously. * Each channel can have at most one agent. * All channels sharing an agent use the same configuration (rules, knowledge, escalation instructions, personality). * Adding or removing channel connections takes effect immediately. *** ## Result type reference Every agent conversation is classified with a result type. These types indicate what the agent did and help diagnose performance issues. | Result type | What happened | Diagnostic signal | | ----------------- | ----------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------ | | Answered | Agent answered using knowledge base content | Successful resolution. High rate indicates good knowledge coverage. | | Clarification | Agent asked for clarification | May indicate vague user requests or rules that need more specific triggers. | | Ticket Created | Agent created a ticket or escalated | Expected for requests requiring human help. High rate may indicate knowledge gaps or overly aggressive escalation. | | Help | Agent explained its own capabilities | Users are unsure what the agent can do. Consider updating channel messaging or agent introduction. | | Missing Knowledge | Agent could not find relevant information | Knowledge gap. Add documentation covering this topic. | | Conversational | General conversation without a specific outcome | Casual interaction. Not necessarily a problem. | | Form Response | Agent presented a form based on a rule | Expected when rules direct form creation. | | Form Field | Agent prompted the user to fill a specific form field | Expected during multi-step form intake. | | Sensitive | Agent detected and handled sensitive information | Security measure activated. Review to confirm correct detection. | | Error | Agent encountered a processing error | Technical issue. Investigate logs for root cause. | | Custom Ticket | Agent created a ticket with a specialized form | Expected for custom form workflows. | | Unpublished | Response was prepared but not sent | May indicate a system issue or interrupted conversation. | *** ## Feedback signals User feedback (thumbs up/down) provides direct signal about response quality: * **Positive feedback** indicates the response was helpful and accurate. * **Negative feedback** indicates a problem. Common causes: incorrect information, missing information, wrong form selected, unhelpful response, or the agent should have escalated instead. When "Create ticket on negative feedback" is enabled (default), negative reactions automatically generate a support ticket for human review. This creates a built-in feedback loop. **Feedback rate** (percentage of responses receiving any feedback) indicates user engagement. Very low feedback rates may mean users are not aware of the feedback mechanism or do not find it convenient. *** ## Debugging patterns ### High "Missing Knowledge" rate The agent frequently cannot find relevant information. * **Likely cause:** Knowledge base gaps. The topics users ask about are not covered in connected knowledge folders. * **Fix:** Review "Missing Knowledge" conversations to identify missing topics. Add documentation to the knowledge base. Verify the correct knowledge folders are connected to the agent. ### High escalation rate The agent creates tickets or escalates to humans more often than expected. * **Likely causes:** Escalation instructions are too aggressive, rules do not cover common scenarios, or knowledge base coverage is insufficient. * **Fix:** Review "Ticket Created" conversations. If the agent could have answered from knowledge, add or improve knowledge content. If the agent escalated due to unclear rules, refine rule trigger conditions. If escalation instructions tell the agent to escalate too eagerly, adjust the thresholds. ### Frequent "ask user" responses The agent asks for clarification too often. * **Likely cause:** Rules lack specificity, so the agent cannot determine intent from the initial message. Alternatively, users are sending very short or ambiguous requests. * **Fix:** Make rule trigger conditions more specific. Add examples of common phrasings to rules. Consider whether conversational form filling might help collect structured data. ### Negative feedback on knowledge answers Users give thumbs down on answers that cite knowledge base articles. * **Likely causes:** Knowledge base content is outdated, inaccurate, or insufficient. The agent may also be citing the wrong article. * **Fix:** Review the cited articles for accuracy. Update or remove outdated content. Check if the agent's knowledge folder connections are correct. ### Wrong form selected The agent presents the wrong form for a user's request. * **Likely cause:** Rule trigger conditions overlap, causing the agent to match the wrong rule. Or a rule's trigger condition is too broad. * **Fix:** Make trigger conditions more distinct across rules. Ensure each form-related rule has specific, non-overlapping trigger criteria. *** ## Upgrade an agent Agents are versioned. When a newer agent version is available, the **Upgrade** action appears on each agent that's still on an older version — both in the agents list (row actions) and in the header of the agent's settings page. Upgrading creates a **new, upgraded copy** of the agent. The new copy is not connected to any channels or queues, so it doesn't respond anywhere until you connect it. Your current agent keeps running unchanged, which lets you review and test the upgrade before switching traffic over. ### When to upgrade * A newer agent version unlocks behavior or capabilities the older version doesn't support. * You want to consolidate on the current runtime so future improvements apply to your agent. * You'd like to retire an older agent but want to validate the upgraded copy in parallel first. ### Run the upgrade From the **Agents** list, open the row actions menu on an eligible agent and select **Upgrade**. You can also open the agent's settings page and click **Upgrade** in the header. The modal prefills a suggested name (the original name with an "Upgraded" suffix). Edit it if you'd like a different name for the new copy. The **Rules** section lists every rule attached to the agent and how it will be carried over: * **Unchanged** — the same rule is re-attached to the upgraded agent. Edits to the rule continue to affect both agents. * **Migrated** — a new copy of the rule is created on the upgraded agent because it needs changes to work on the new version. The original rule stays as-is on the original agent. The **Tool changes** section lists tools that don't exist in the new version but have a known equivalent (or are safe to remove). These are handled for you. Rules affected by an automatic change are flagged for review on the upgraded agent. The **Choose replacements** section appears when one or more tools used by the agent aren't available in the new version and don't have a known equivalent. For each one, pick a replacement tool from the list or select **Remove (no replacement)** to drop it. You can't complete the upgrade until every unavailable tool has a decision. Click **Upgrade**. Ravenna creates the upgraded copy and opens it so you can review the configuration. Add the upgraded agent to the channels and queues you want it to handle, then remove those connections from the original agent when you're ready to cut over. Only one upgraded copy of a given agent can exist at a time. If an upgraded copy already exists, finish reviewing or delete it before starting another upgrade. *** ## Optimization recommendations * **Track result distribution weekly.** A sudden shift in result types (e.g., spike in "Missing Knowledge" or "Error") signals a configuration or content issue that needs investigation. * **Review negative feedback conversations first.** These are the highest-signal data points for improving agent quality. * **Use "Answered" rate as a primary metric.** This directly measures how often the agent successfully resolves requests without human intervention. * **Monitor escalation rate as a secondary metric.** Some escalation is expected and healthy. The goal is not zero escalation, but appropriate escalation. * **After making changes, compare before and after.** Check whether rule updates, knowledge additions, or escalation instruction changes actually improved the result distribution.