> ## Documentation Index > Fetch the complete documentation index at: https://docs.tavus.io/llms.txt > Use this file to discover all available pages before exploring further. # Customer Service Agent > Engage in real-time customer support conversations that adapt to user emotions and behavior. ## Customer Service Agent Configuration This example uses the [tools registry](/sections/conversational-video-interface/pal/tools): tools are created once at `/v2/tools` and attached to the PAL. Tools here use the default **app-message** delivery, so calls arrive as `conversation.tool_call` events for your frontend to handle. The agent uses two tools - an LLM tool that logs the customer's issue, and a vision tool that reports the user's emotional state when Raven sees a strong cue. ```json resolve_customer_issue (LLM tool) theme={null} { "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" } } }, "origin": "llm" } ``` ```json user_emotional_state (vision tool) theme={null} { "name": "user_emotional_state", "description": "Report the user's emotional state as inferred from body language and voice tone. Trigger when facial expressions or posture indicate a strong emotional state such as frustration, confusion, or calmness.", "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)" } } }, "origin": "vision" } ``` The PAL itself no longer defines tools inline - it sets the models and ambient queries, and the tools are attached separately: ```json PAL configuration [expandable] theme={null} { "pal_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_face_id": "r90bbd427f71", "layers": { "tts": { "tts_engine": "cartesia", "tts_emotion_control": true }, "llm": { "model": "tavus-gpt-oss", "speculative_inference": true }, "perception": { "perception_model": "raven-1", "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?" ] }, "conversational_flow": { "turn_detection_model": "sparrow-1", "turn_taking_patience": "low", "pal_interruptibility": "medium" } } } ``` This PAL is configured to handle real product or service issues with empathy. It includes: * **PAL 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. * **PAL Layer**: * **LLM Layer**: The attached `resolve_customer_issue` tool gathers: * `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-1` model to watch for signs like fidgeting, slouching, or facial expressions. When the user appears upset, the attached `user_emotional_state` vision tool fires 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. * **Conversational Flow Layer**: Uses `sparrow-1` turn detection model with low turn-taking patience for fast responses and medium face interruptibility for balanced conversation flow. ## Create a Conversation with the Customer Service Agent PAL Create each tool at `/v2/tools`. The response returns a `tool_id` (e.g. `t1234567890`) you'll attach to the PAL in Step 3. ```sh resolve_customer_issue [expandable] theme={null} curl --request POST \ --url https://tavusapi.com/v2/tools \ --header 'Content-Type: application/json' \ --header 'x-api-key: ' \ --data '{ "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" } } }, "origin": "llm" }' ``` ```sh user_emotional_state [expandable] theme={null} curl --request POST \ --url https://tavusapi.com/v2/tools \ --header 'Content-Type: application/json' \ --header 'x-api-key: ' \ --data '{ "name": "user_emotional_state", "description": "Report the user'\''s emotional state as inferred from body language and voice tone. Trigger when facial expressions or posture indicate a strong emotional state such as frustration, confusion, or calmness.", "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)" } } }, "origin": "vision" }' ``` Create the PAL using the following request. Note there are no inline tools - they're attached in the next step. ```sh cURL [expandable] theme={null} curl --request POST \ --url https://tavusapi.com/v2/pals \ --header 'Content-Type: application/json' \ --header 'x-api-key: ' \ --data '{ "pal_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_face_id": "r90bbd427f71", "layers": { "tts": { "tts_engine": "cartesia", "tts_emotion_control": true }, "llm": { "model": "tavus-gpt-oss", "speculative_inference": true }, "perception": { "perception_model": "raven-1", "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?" ] }, "conversational_flow": { "turn_detection_model": "sparrow-1", "turn_taking_patience": "low", "pal_interruptibility": "medium" } } }' ``` Replace `` with your actual API key. You can generate one in the PAL Maker. Attach both tools to the PAL by their `tool_id`s. Vision tools require `perception_model: "raven-1"` on the PAL, which the configuration above already sets. ```sh cURL theme={null} curl --request POST \ --url https://tavusapi.com/v2/pals//tools \ --header 'Content-Type: application/json' \ --header 'x-api-key: ' \ --data '{ "tool_ids": ["", ""] }' ``` Use the following request body example: ```shell cURL theme={null} curl --request POST \ --url https://tavusapi.com/v2/conversations \ --header 'Content-Type: application/json' \ --header 'x-api-key: ' \ --data '{ "pal_id": "" }' ``` * Replace `` with your actual API key. * Replace `` with the ID of the PAL you created using the Customer Service Agent configuration. Click the link in the ***`conversation_url`*** field to join the conversation: ```json theme={null} { "conversation_id": "c7f3fc6d766f", "conversation_name": "New Conversation 1747719531479", "conversation_url": "", "status": "active", "callback_url": "", "created_at": "2025-05-20T05:38:51.501467Z" } ```