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.
Implement a simulated A2A communication module for your agents. This module should demonstrate message encryption (e.g., using a simple symmetric key simulation) and secure message passing. Show how agents exchange design proposals or data access requests, with only authorized recipients able to decrypt and process the messages. Use either Claude Sonnet 4 or Mistral Large 2 for agent reasoning in this process.
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 is mostly narrative and instruction-driven, so you can adapt examples and output constraints first without disturbing the structure.
Secure Agentic System Design for Privacy-Centric Services
This challenge involves designing a secure, privacy-preserving multi-agent system for a hypothetical next-gen personal data management platform. The system needs to ensure user data is handled with utmost discretion, mimicking principles of zero-knowledge proofs where data is verified without being revealed. You will utilize AutoGen for orchestrating autonomous conversations among agents focused on system design and architectural choices. OpenAI Swarm will manage agent deployment and coordination in a decentralized fashion. The core will involve implementing A2A protocol for encrypted agent communication and MCP tool integration for controlled, auditable access to sensitive data stores, ensuring privacy by design and by default.
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.