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Scenarios
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Use Case
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Who Benefits
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What the AI Chat does
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Why it matters
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Validate data source configuration changes
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Service administrators use AI Chat to confirm data source updates. After adding new objects or fields, they ask AI Chat relevant questions to verify that the changes are reflected correctly in AI responses.
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Service administrators and AI administrators
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Responds to queries using the latest configured data sources and fields.
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Confirms that newly added objects or fields are available to AI without navigating to other tools or environments.
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Verify agent guideline updates
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AI administrators update agent guidelines and use AI Chat to check whether responses reflect the new instructions. This helps validate guideline changes before enabling AI for broader use.
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AI administrators and service administrators
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Generates responses based on the latest agent guidelines.
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Ensures that updated instructions take effect immediately and influence AI behavior as intended.
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Test AI Action execution
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Service administrators use AI Chat to trigger AI Actions and confirm that they execute correctly. AI Chat provides a quick way to test actions before exposing them to end users.
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Service administrators and operations teams
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Triggers configured AI Actions through conversational input.
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Allows teams to validate AI Action behavior and execution results without switching contexts.
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Evaluate AI responses to real‑world user queries
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Service teams use AI Chat to test how AI responds to common user questions. This allows them to assess accuracy and relevance and make adjustments before deployment.
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Service administrators and service managers
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Responds to natural‑language queries using configured agents, data sources, and actions.
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Helps teams validate response quality and relevance before deploying AI to client applications.
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Troubleshoot AI setup issues in a single environment
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AI administrators use AI Chat to identify configuration issues by observing AI behavior directly. This reduces the need to move between setup pages and test environments during troubleshooting.
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AI administrators and support teams
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Reflects current AI configuration behavior in real time.
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Simplifies troubleshooting by allowing teams to identify and correct issues without switching between environments.
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