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.
Outline a strategy for deploying and scaling your multi-agent robotaxi coordination system using OpenAI Swarm. Detail how Langroid agents will be managed across a large urban network, how A2A communication channels will be maintained at scale, and how monitoring will be implemented to ensure optimal performance and resilience across thousands of robotaxis and dynamic traffic conditions.
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 source structure until you know which part of the prompt is actually driving the result quality.
Change domain facts, examples, and tool context first before you rewrite the instruction scaffold.
Validate one failure mode at a time so prompt changes stay attributable instead of getting noisy.
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.
Multi-Agent Protocol for Smart City Robotaxi Coordination
This challenge focuses on designing a multi-agent system for smart city traffic management. You will build an A2A protocol-enabled system using Langroid agents powered by DeepSeek to coordinate different robotaxi fleets. The system must optimize traffic flow, dynamically re-route vehicles, and resolve conflicts by leveraging real-time traffic data, extended thinking for predictive routing, and few-shot learning to adapt to evolving urban conditions. Emphasis will be placed on seamless inter-fleet cooperation and robust decision-making in a complex, dynamic environment. This project requires implementing sophisticated agent communication and autonomous reasoning. You will integrate MCP tools for real-time urban data streams and orchestrate large-scale agent deployments using OpenAI Swarm, pushing the envelope for urban mobility solutions.
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.