AI Agent Orchestration with Claude

Direct Anthropic SDK (Python)

import anthropic

client = anthropic.Anthropic()

# Define tools Claude can use
tools = [
    {
        "name": "search_knowledge_base",
        "description": "Search internal documentation and knowledge base",
        "input_schema": {
            "type": "object",
            "properties": {
                "query": {"type": "string", "description": "Search query"},
                "category": {"type": "string", "enum": ["technical", "billing", "general"]}
            },
            "required": ["query"]
        }
    },
    {
        "name": "get_customer_record",
        "description": "Retrieve customer account information by email",
        "input_schema": {
            "type": "object",
            "properties": {
                "email": {"type": "string"}
            },
            "required": ["email"]
        }
    }
]

def run_support_agent(customer_query: str, customer_email: str) -> str:
    messages = [
        {
            "role": "user",
            "content": f"Customer email: {customer_email}\nQuery: {customer_query}"
        }
    ]

    while True:
        response = client.messages.create(
            model="claude-3-5-sonnet-20241022",
            max_tokens=2048,
            system="You are a helpful customer support agent. Use the available tools to look up information before responding.",
            tools=tools,
            messages=messages
        )

        if response.stop_reason == "end_turn":
            # Extract text response
            return next(b.text for b in response.content if b.type == "text")

        if response.stop_reason == "tool_use":
            # Process tool calls
            tool_results = []
            for block in response.content:
                if block.type == "tool_use":
                    # In production: call actual tools here
                    result = {"found": True, "data": f"Mock result for {block.name}"}
                    tool_results.append({
                        "type": "tool_result",
                        "tool_use_id": block.id,
                        "content": str(result)
                    })

            # Add assistant response and tool results to messages
            messages.append({"role": "assistant", "content": response.content})
            messages.append({"role": "user", "content": tool_results})

result = run_support_agent(
    "My subscription charged twice this month",
    "user@example.com"
)
print(result)