Operator-ready prompt for reuse, tuning, and workspace runs.
This item is set up for developers who want to inspect the original language, fork it into Workspace, and adapt the evidence model without losing the source prompt structure.
Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.
Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.
Swap domain facts, examples, and any hard-coded entities for your own context.
Tighten the evidence or verification requirement if this is headed toward production.
Decide which failure mode you want to evaluate first before you branch the prompt.
This prompt already carries implementation detail, tool context, and a final-output instruction. Keep that structure intact when you tune it, or your comparison runs get noisy fast.
Open this prompt inside Workspace when you want a live iteration loop.
Copy for quick reuse, or run it in Workspace to keep prompt variants, model settings, and prompt-history changes in one place.
Structured source with 7 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
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 branch your edits in a predictable order so the next prompt run is easier to evaluate.
Preserve the role framing, objective, and reporting structure so comparison runs stay coherent.
Swap in your own domain constraints, anomaly thresholds, and examples before you branch variants.
Check whether the prompt asks for the right evidence, confidence signal, and escalation path.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.
This prompt already mixes executable detail with instructions, so the safest path is to tune examples and interfaces before you rewrite the overall scaffold.
Real-time Personalized Sales Guidance
This challenge involves developing a sophisticated multi-agent system using the Claude Agents SDK to provide personalized, real-time sales guidance. The system will act as an intelligent sales assistant, offering deal-specific recommendations, objection handling strategies, and next-step actions to sales representatives. The core of this system will be its ability to understand complex sales conversations and leverage extensive knowledge for strategic advice. Developers will design agents with extended thinking capabilities using Claude 4 Sonnet, which will then interact with a specialized 'Knowledge Agent' powered by Llama 4 Maverick for in-depth data retrieval and synthesis from various sales enablement resources. The system will integrate with a secure access platform like Aembit to ensure controlled and auditable access to sensitive CRM data. Libretto will be used for intelligent model routing and A/B testing of different guidance strategies, optimizing performance. Bito AI will serve as the conversational interface for sales reps, providing instant, context-aware advice. The overall agent orchestration will leverage Letta AI's capabilities for managing agent lifecycles and tool orchestration.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.