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Initialize Claude Agents SDK with Llama 4 Maverick
Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.
Linked challenge: Real-time Personalized Sales Guidance
Format
Code-aware
Lines
7
Sections
1
Linked challenge
Real-time Personalized Sales Guidance
Prompt source
Original prompt text with formatting preserved for inspection.
7 lines
1 sections
No variables
1 code block
Begin by setting up your Claude Agents SDK environment. Define a main `SalesGuidanceAgent` powered by Claude 4 Sonnet for strategic reasoning. This agent should orchestrate a `KnowledgeAgent` powered by Llama 4 Maverick, responsible for synthesizing information from internal knowledge bases (simulated). The `SalesGuidanceAgent` will have access to tools for interacting with CRM (via Aembit) and a real-time communication tool (via Bito AI). Use the following snippet to start: ```python
import anthropic
from anthropic.agents import AnthropicAgent, Tool, AgentState # Example, actual SDK might vary # Placeholder for Llama 4 Maverick inference client
class Llama4MaverickClient: def query(self, prompt: str) -> str: # Simulate Llama 4 Maverick call return f"Llama 4 Maverick processed: {prompt}" llama_client = Llama4MaverickClient()
claude_client = anthropic.Anthropic(api_key='YOUR_CLAUDE_API_KEY') class CRMAccessTool(Tool): name = 'crm_access_tool' description = 'Accesses secure CRM data via Aembit for customer details and deal history.' parameters = {'customer_id': {'type': 'string', 'description': 'ID of the customer'}} def use(self, customer_id: str) -> str: # Simulate Aembit-protected CRM access return f"Retrieved secure CRM data for {customer_id} via Aembit." # Define the SalesGuidanceAgent and KnowledgeAgent...
```
Focus on defining the agents' initial roles, goals, and the communication flow between them. Ensure Llama 4 Maverick is used for specific knowledge synthesis tasks by the `KnowledgeAgent`.Adaptation plan
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