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 the agent's workflow for handling a robotaxi incident report. The agent should first use the `simulator_api` to get incident details, then `computer_use` to retrieve relevant safety policies and regulations. Finally, using Claude Opus's advanced reasoning, it should summarize the incident, identify root causes, and propose a new or updated operational procedure. Outline the conversational flow or the structured steps the agent would take.
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 role framing, objective, and reporting structure so comparison runs stay coherent.
Swap in your own domain constraints, anomaly thresholds, and examples before you branch variants.
Check whether the prompt asks for the right evidence, confidence signal, and escalation path.
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.
Agent for Robotaxi Safety Policy Analysis & Dynamic Procedure Generation
Develop an advanced agent using Anthropic's Claude Agents SDK to analyze real-time robotaxi incident data, cross-reference it with complex safety regulations, and dynamically generate or adapt operational procedures. Inspired by the Waymo incident and upcoming UK regulations, this challenge focuses on building a highly reliable, safety-critical agent that can interpret regulatory documents, learn from incidents, and output actionable safety protocols. The agent will leverage Claude Opus's extended thinking and 'computer use' capabilities to process vast amounts of unstructured text and adapt to evolving regulatory landscapes.
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.