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.
Develop a simplified A2A communication module between your 'Driver Agent' and a 'Regulatory Agent'. The 'Regulatory Agent' should be responsible for checking geo-fencing rules or speed limits based on the current location received from the 'Driver Agent'. Design the message format and communication flow. The 'Regulatory Agent' should use RAG to query a knowledge base of regulations.
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.
Autonomous Robotaxi Decision Agent
Design and implement an advanced agentic system capable of making real-time, safety-critical decisions for autonomous robotaxis. This challenge focuses on building a robust, stateful decision-making agent that can handle complex traffic scenarios, unexpected events, and regulatory compliance by integrating real-time sensor data and regulatory knowledge bases. The system will leverage extended thinking capabilities and graph-based workflows to ensure safe and efficient operation within a simulated environment. The core of this challenge involves orchestrating a multi-agent system where a central 'Driver Agent' (powered by Gemini 2.5 Pro in Deep Think mode) uses LangGraph to manage dynamic state and decision paths. This agent will communicate with 'Sensor Agents' and 'Regulatory Agents' via a simplified A2A protocol. Tool integration will simulate vehicle controls (acceleration, braking, steering) and external data feeds (traffic updates, weather forecasts, geo-fencing policies).
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.