Back to Prompt Library
planning

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

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Preserve the role framing, objective, and reporting structure so comparison runs stay coherent.

Tune next

Swap in your own domain constraints, anomaly thresholds, and examples before you branch variants.

Verify after

Check whether the prompt asks for the right evidence, confidence signal, and escalation path.