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 1 active lines to adapt.
Already linked to a challenge workflow.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Design a CrewAI agent team for customer service, including a 'Router Agent', 'Technical Support Agent', 'Billing Agent', and 'Sales Agent'. Define their roles, goals, and specific tasks. Outline a workflow where the Router Agent triages incoming requests and hands off to appropriate specialist agents, potentially involving multi-agent collaboration for complex issues.
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 is mostly narrative and instruction-driven, so you can adapt examples and output constraints first without disturbing the structure.
Client Service Orchestration Agent
This challenge tasks developers with building a sophisticated multi-agent system designed to orchestrate seamless customer support across diverse communication channels (e.g., chat, email, voice). The system will leverage CrewAI for role-based agent orchestration, with specialized agents communicating via an A2A (Agent-to-Agent) Protocol. Crucially, it will integrate with simulated enterprise systems (CRM, knowledge bases) using an MCP (server for secure, structured tool invocation. This project focuses on creating a 'supervisory' agent that routes inquiries to the most appropriate 'specialist' agents. These specialist agents (e.g., 'Technical Support Agent', 'Billing Agent', 'Sales Agent'), powered by Claude Opus 4.1 for nuanced understanding and response generation, will collaborate, share context, and utilize MCP-enabled tools to resolve complex customer issues efficiently. The challenge emphasizes secure, structured inter-agent communication and robust enterprise system integration.
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.