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.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Design and implement a simplified A2A (Agent-to-Agent) communication protocol within your CrewAI setup. This protocol should facilitate secure handoffs of context and unresolved issues between agents. Describe how an agent 'A' would signal to agent 'B' that a task needs to be passed, including the structured data (e.g., customer_id, current_context, remaining_tasks) that would be transferred. Provide a Python code example for a basic message passing function between agents.
Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Hold the task contract and output shape stable so generated implementations remain comparable.
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.
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.
Multi-Agent Customer Support with Claude Opus 4.1 & MCP for CRM
Following news of AI agents for customer support raising significant funding, this challenge tasks developers with building an advanced, multi-agent customer support system. The system will leverage Claude Opus 4.1 for sophisticated reasoning and empathetic communication, orchestrated by CrewAI to create specialized, role-based agents (e.g., 'Initial Greeter,' 'Technical Support Specialist,' 'Billing Expert,' 'Resolution Agent'). These agents will collaborate seamlessly using an A2A (Agent-to-Agent) protocol for handoffs and information sharing. Crucially, the system will integrate with simulated enterprise Customer Relationship Management (CRM) and knowledge base systems via a Model Context Protocol (MCP). This MCP layer will ensure secure, structured access to customer data, past interactions, and product information, enabling agents to provide highly personalized and accurate support. The challenge emphasizes building a resilient, scalable, and ethically sound customer service automation solution that can handle complex inquiries requiring multiple agent interactions and external data lookups.
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.