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 integrate a multi-agent system using Microsoft AutoGen. Create an 'Analyst' agent and a 'Researcher' agent. The 'Analyst' should receive high-level queries from the AI SDK agent and delegate specific data gathering tasks to the 'Researcher'. Ensure results from AutoGen agents are synthesized and returned to the AI SDK agent for final voice output. Define a tool for the 'Researcher' to simulate web search for market data.
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.
Real-time Voice Assistant for Market Intelligence
Develop a sophisticated real-time voice assistant capable of performing competitive market intelligence and product analysis. This challenge focuses on building a responsive, multi-modal agent system that leverages the AI SDK for seamless streaming interactions and sophisticated tool orchestration. The core reasoning will be powered by Google's Gemini 3 Flash, providing rapid and accurate insights based on voice input. The system will integrate Microsoft AutoGen to spin up specialized, scriptable agents that handle deep-dive research tasks, collaborating with the primary voice agent. Security and data integrity are paramount, so LatticeFlow AI will be utilized to implement robust model safety policies and secure data pipelines. For comprehensive monitoring and evaluation, LangFuse will be integrated to trace agent interactions and performance metrics, ensuring the system operates efficiently and reliably. Retell AI will provide the real-time voice-to-text and text-to-speech capabilities, enabling natural language interactions for the end-user. This challenge emphasizes cutting-edge multi-agent orchestration, real-time voice processing, and advanced AI engineering practices to deliver a high-performance, secure, and observable AI application.
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.