> ## Documentation Index
> Fetch the complete documentation index at: https://documentation.uponai.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Define Resolution Criteria

> Set up AI-evaluated conditions and performance metrics that determine whether a call is considered successful.

Resolution criteria define what makes a call successful. AI QA evaluates every analyzed call against these criteria.

<Steps>
  <Step title="Add AI Evaluated Conditions">
    Custom qualitative criteria evaluated by AI based on call transcripts and context.

    **Name** — A short identifier (e.g., "Call resolved", "Customer satisfied", "Issue escalated properly").

    **Prompt Description** — The prompt the AI uses to evaluate whether the condition was met.

    Example: `"AI agent was able to resolve user's query"`

    **Best practices:**

    * Be specific about what success looks like
    * Include relevant context about the call type or use case
    * Use clear, unambiguous language

    Click **+ Add** to add multiple conditions. Each is evaluated independently.
  </Step>

  <Step title="Add Performance Metrics">
    Quantitative thresholds calls must meet to be considered successful.

    | Metric                         | Description                                                           |
    | ------------------------------ | --------------------------------------------------------------------- |
    | **Latency**                    | End-to-end delay between user speaking and agent beginning response   |
    | **User Sentiment**             | Emotional state of the caller inferred from speech, tone, and pitch   |
    | **Agent Sentiment**            | Emotional tone expressed by the AI during speech                      |
    | **Interruptions**              | Count of times user interrupted the agent                             |
    | **Transcription**              | WER and number of mistranscribed entities                             |
    | **Agent Hallucination**        | Rate at which the agent hallucinated                                  |
    | **Tool Call Inaccuracy**       | Rate at which the agent invoked incorrect tools                       |
    | **Node Transition Inaccuracy** | Rate of incorrect node transitions                                    |
    | **Agent Naturalness**          | How human-like the agent sounded (pronunciation, pacing, turn-taking) |

    Click **+ Add** to add multiple metrics.

    <Note>
      A call is considered successful only if it meets **all** defined criteria across both AI Evaluated Conditions and Performance Metrics.
    </Note>
  </Step>

  <Step title="Configure Weighted Scoring (optional)">
    Enable **Weighted Scoring** to assign different weights to your criteria — giving more importance to certain conditions or metrics.

    **When enabled:** Assign weights to each condition and metric, then set a **Success Criteria** threshold.

    **When disabled:** All criteria are treated equally — a call must meet all conditions.

    Use weighted scoring when some criteria are more important than others. For example, weight "Call resolved" higher than "Customer satisfaction" if resolution is your primary goal.
  </Step>

  <Step title="Save and Run QA">
    Click **Save and Run QA** to finalize and start analysis.

    If you encounter a validation error, review all conditions and metrics to ensure required fields are filled and thresholds are set.
  </Step>
</Steps>