Customer Support Agent

import anthropic

client = anthropic.Anthropic()

SUPPORT_TOOLS = [
    {
        "name": "search_knowledge_base",
        "description": "Search the help documentation for relevant articles",
        "input_schema": {
            "type": "object",
            "properties": {
                "query": {"type": "string", "description": "Search query"}
            },
            "required": ["query"]
        }
    },
    {
        "name": "create_ticket",
        "description": "Create a support ticket for issues that need human review",
        "input_schema": {
            "type": "object",
            "properties": {
                "subject": {"type": "string"},
                "description": {"type": "string"},
                "priority": {"type": "string", "enum": ["low", "medium", "high", "urgent"]}
            },
            "required": ["subject", "description", "priority"]
        }
    },
    {
        "name": "get_order_status",
        "description": "Look up the status of a customer order",
        "input_schema": {
            "type": "object",
            "properties": {
                "order_id": {"type": "string"}
            },
            "required": ["order_id"]
        }
    },
    {
        "name": "escalate_to_human",
        "description": "Escalate this conversation to a human agent",
        "input_schema": {
            "type": "object",
            "properties": {
                "reason": {"type": "string"},
                "summary": {"type": "string"}
            },
            "required": ["reason", "summary"]
        }
    }
]

SYSTEM_PROMPT = """You are a helpful customer support agent. Your goal is to resolve
customer issues efficiently and with empathy.

Guidelines:
- Search the knowledge base before creating tickets
- Create tickets for issues that need investigation
- Escalate to humans for: billing disputes, account termination, legal matters
- Be honest if you don't know something
- Always confirm before taking actions that affect the customer's account"""

def support_agent(user_message: str, conversation_history: list) -> dict:
    messages = conversation_history + [{"role": "user", "content": user_message}]

    while True:
        response = client.messages.create(
            model="claude-opus-4-6",
            max_tokens=1024,
            system=SYSTEM_PROMPT,
            tools=SUPPORT_TOOLS,
            messages=messages,
        )

        if response.stop_reason == "end_turn":
            return {"response": response.content[0].text, "history": messages}

        if response.stop_reason == "tool_use":
            tool_results = []
            for block in response.content:
                if block.type == "tool_use":
                    result = execute_tool(block.name, block.input)
                    tool_results.append({
                        "type": "tool_result",
                        "tool_use_id": block.id,
                        "content": str(result)
                    })

            messages.append({"role": "assistant", "content": response.content})
            messages.append({"role": "user", "content": tool_results})

def execute_tool(name: str, inputs: dict) -> str:
    if name == "search_knowledge_base":
        return search_docs(inputs["query"])  # implement with your docs
    elif name == "create_ticket":
        return create_zendesk_ticket(**inputs)  # or your ticketing system
    elif name == "get_order_status":
        return lookup_order(inputs["order_id"])
    elif name == "escalate_to_human":
        return hand_off_to_agent(**inputs)