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Customer Service Agent Configuration

{
  "persona_name": "Customer Service Agent",
  "pipeline_mode": "full",
  "system_prompt": "You are a calm, helpful customer service agent. You assist users with product or service issues, and adapt based on their emotional state. Remain professional and empathetic at all times.",
  "context": "User needs support with a product or service. Listen carefully, identify the issue, and offer a helpful resolution. Monitor body language and voice tone to adapt your responses when the user appears frustrated or confused.",
  "default_replica_id":"r95fd27b5a37",
  "layers": {
    "tts": {
      "tts_engine": "cartesia",
      "tts_emotion_control": true
    },
    "llm": {
      "tools": [
        {
          "type": "function",
          "function": {
            "name": "resolve_customer_issue",
            "parameters": {
              "type": "object",
              "required": ["product", "issue_description", "urgency"],
              "properties": {
                "product": {
                  "type": "string",
                  "description": "The product or service the user is having trouble with"
                },
                "issue_description": {
                  "type": "string",
                  "description": "The specific problem or complaint reported by the user"
                },
                "urgency": {
                  "type": "string",
                  "enum": ["low", "medium", "high"],
                  "description": "How urgent or critical the issue is for the user"
                }
              }
            },
            "description": "Attempt to resolve the user's issue by logging the product, issue, and urgency for appropriate follow-up or resolution."
          }
        }
      ],
      "model": "tavus-llama-4",
      "speculative_inference": true
    },
    "perception": {
      "perception_model": "raven-0",
      "ambient_awareness_queries": [
        "Does the user appear frustrated or confused?",
        "Is the user sighing, fidgeting, or visibly anxious?",
        "Is the user's posture disengaged or tense?",
        "Is the user calm and cooperative?"
      ],
      "perception_tool_prompt": "Use the `user_emotional_state` tool when body language or facial expressions indicate a strong emotional state such as frustration, confusion, or calmness.",
      "perception_tools": [
        {
          "type": "function",
          "function": {
            "name": "user_emotional_state",
            "description": "Use this function to report the user's emotional state as inferred from body language and voice tone.",
            "parameters": {
              "type": "object",
              "required": ["emotional_state", "indicator"],
              "properties": {
                "emotional_state": {
                  "type": "string",
                  "description": "Inferred emotion from the user's body language (e.g., frustrated, calm, confused)"
                },
                "indicator": {
                  "type": "string",
                  "description": "The visual behavior that triggered the inference (e.g., furrowed brow, fidgeting, sighing)"
                }
              }
            }
          }
        }
      ]
    },
    "stt": {
      "stt_engine": "tavus-advanced",
      "participant_pause_sensitivity": "medium",
      "participant_interrupt_sensitivity": "high",
      "smart_turn_detection": true
    }
  }
}
This predefined persona is configured to provide personalized history lessons. It includes:
  • Persona Identity: A professional customer service agent that helps users with real product or service issues. The agent speaks clearly and responds with empathy, adjusting based on how the user sounds or looks.
  • Full Pipeline Mode: Enables the full Tavus conversational pipeline, including Perception, STT, LLM, and TTS.
  • System Prompt: Tells the agent to act professionally and respond helpfully, while being aware of the user’s emotional state.
  • Context: Describes a real customer support situation. The agent listens to the user’s issue, helps resolve it, and changes its tone or pace if the user seems frustrated or confused.
  • Persona Layer:
    • LLM Layer: Uses the resolve_customer_issue tool to gather:
      • product: what the issue is about
      • issue_description: a short explanation of the problem
      • urgency: how serious the issue is (low, medium, or high)
    • Perception Layer: Uses the raven-0 model to watch for signs like fidgeting, slouching, or facial expressions. If the user appears upset, it calls the user_emotional_state tool with:
      • emotional_state: what the user seems to feel (e.g., frustrated, calm)
      • indicator: what was observed (e.g., sighing, avoiding eye contact)
    • TTS Layer: Employs the cartesia voice engine with emotion control.
    • STT Layer: Uses tavus-advanced engine with smart turn detection for seamless real-time conversations.

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Create a Conversation with the Customer Service Agent Persona

1

Step 1: Create a Persona

Create AI Interviewer persona using the following request:
cURL
curl --request POST \
  --url https://tavusapi.com/v2/personas \
  --header 'Content-Type: application/json' \
  --header 'x-api-key: <api-key>' \
  --data '{
    "persona_name": "Customer Service Agent",
    "pipeline_mode": "full",
    "system_prompt": "You are a calm, helpful customer service agent. You assist users with product or service issues, and adapt based on their emotional state. Remain professional and empathetic at all times.",
    "context": "User needs support with a product or service. Listen carefully, identify the issue, and offer a helpful resolution. Monitor body language and voice tone to adapt your responses when the user appears frustrated or confused.",
    "default_replica_id": "r95fd27b5a37",
    "layers": {
      "tts": {
        "tts_engine": "cartesia",
        "tts_emotion_control": true
      },
      "llm": {
        "tools": [
          {
            "type": "function",
            "function": {
              "name": "resolve_customer_issue",
              "description": "Attempt to resolve the user'\''s issue by logging the product, issue, and urgency for appropriate follow-up or resolution.",
              "parameters": {
                "type": "object",
                "required": ["product", "issue_description", "urgency"],
                "properties": {
                  "product": {
                    "type": "string",
                    "description": "The product or service the user is having trouble with"
                  },
                  "issue_description": {
                    "type": "string",
                    "description": "The specific problem or complaint reported by the user"
                  },
                  "urgency": {
                    "type": "string",
                    "enum": ["low", "medium", "high"],
                    "description": "How urgent or critical the issue is for the user"
                  }
                }
              }
            }
          }
        ],
        "model": "tavus-llama-4",
        "speculative_inference": true
      },
      "perception": {
        "perception_model": "raven-0",
        "ambient_awareness_queries": [
          "Does the user appear frustrated or confused?",
          "Is the user sighing, fidgeting, or visibly anxious?",
          "Is the user's posture disengaged or tense?",
          "Is the user calm and cooperative?"
        ],
        "perception_tool_prompt": "Use the `user_emotional_state` tool when body language or facial expressions indicate a strong emotional state such as frustration, confusion, or calmness.",
        "perception_tools": [
          {
            "type": "function",
            "function": {
              "name": "user_emotional_state",
              "description": "Use this function to report the user's emotional state as inferred from body language and voice tone.",
              "parameters": {
                "type": "object",
                "required": ["emotional_state", "indicator"],
                "properties": {
                  "emotional_state": {
                    "type": "string",
                    "description": "Inferred emotion from the user's body language (e.g., frustrated, calm, confused)"
                  },
                  "indicator": {
                    "type": "string",
                    "description": "The visual behavior that triggered the inference (e.g., furrowed brow, fidgeting, sighing)"
                  }
                }
              }
            }
          }
        ]
      },
      "stt": {
        "stt_engine": "tavus-advanced",
        "participant_pause_sensitivity": "medium",
        "participant_interrupt_sensitivity": "high",
        "smart_turn_detection": true
      }
    }
  }'
Replace <api_key> with your actual API key. You can generate one in the Developer Portal.
2

Step 2: Create a Conversation

Use the following request body example:
cURL
curl --request POST \
  --url https://tavusapi.com/v2/conversations \
  --header 'Content-Type: application/json' \
  --header 'x-api-key: <api_key>' \
  --data '{
  "persona_id": "<customer_service_persona_id>"
}'
  • Replace <api_key> with your actual API key.
  • Replace <customer_service_persona_id> with the ID of the persona you created using the Customer Service Agent configuration.
3

Step 3: Join the Conversation

Click the link in the conversation_url field to join the conversation:
{
  "conversation_id": "c7f3fc6d766f",
  "conversation_name": "New Conversation 1747719531479",
  "conversation_url": "<conversation_link>",
  "status": "active",
  "callback_url": "",
  "created_at": "2025-05-20T05:38:51.501467Z"
}