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.
Refine the agent system to incorporate adaptive thinking. For urgent queries identified by the Triage Agent, allocate a higher reasoning budget (e.g., more LLM calls, deeper RAG search) to the Information and Empathy Agents. Furthermore, implement explicit checks or rules to ensure all responses adhere to ethical AI principles and patient privacy, especially when handling sensitive health information.
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.
Patient Triage & Communication Agent
This challenge tasks developers with building an advanced AI copilot designed to manage patient communications, specifically focusing on initial triage, information dissemination, and empathetic responses. The system will leverage a multi-agent architecture to simulate a healthcare support team, integrating seamlessly with communication platforms like WhatsApp. Participants will focus on ethical AI, data privacy, and the robust integration of MCP-enabled tools for secure access to patient data and medical knowledge bases. The solution must demonstrate adaptive thinking, prioritizing urgent patient queries and providing accurate, compassionate support. This system will orchestrate a team of specialized agents, each with a distinct role: a Triage Agent for initial assessment, an Information Agent for retrieving medical facts, and an Empathy Agent for crafting supportive messages. Developers will implement RAG over a simulated medical knowledge base to ensure factual accuracy and utilize adaptive thinking budgets to allocate processing power based on query complexity and urgency. The core will involve designing secure, MCP-enabled communication channels and tool integrations to interact with a mock WhatsApp API, ensuring patient data is handled responsibly.
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.