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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.
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Tighten the evidence or verification requirement if this is headed toward production.
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Structured source with 30 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Using the Claude Agents SDK, initialize a `SafetyComplianceAgent`. This agent should have access to a 'computer use' tool to interact with simulated document databases and a 'simulator_api' tool to get incident details. Provide the Python code for agent initialization and definition of these tools. Assume you have `claude_api_key` configured.
```python
import anthropic
from anthropic.agents import Agent, tool
@tool
def computer_use(command: str) -> str:
"""Simulates executing a command on a computer, e.g., to read files or query databases."""
print(f"Executing computer command: {command}")
# In a real scenario, this would interact with a filesystem or a database.
if "read_policy" in command:
return "Safety Policy 3.1: Pedestrian Zones. Max speed 10MPH. Child Safety Guidelines 1.2: Be aware of school hours."
return "Command executed successfully (simulated)."
@tool
def simulator_api(query: str) -> str:
"""Queries a simulated robotaxi incident database."""
print(f"Querying simulator API: {query}")
if "incident_details" in query:
return "Incident ID 123: Voyager-7, struck child, 6MPH, 3:15 PM, near school, delayed braking."
return "Simulator data (simulated)."
def create_safety_agent():
client = anthropic.Anthropic(api_key="YOUR_CLAUDE_API_KEY")
agent = Agent(
client=client,
tools=[computer_use, simulator_api],
model="claude-3-5-opus-20240620",
system_prompt="You are an expert robotaxi safety compliance officer. Your task is to analyze incidents, interpret safety policies, and propose updated procedures."
)
return agent
# agent = create_safety_agent()
```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 already mixes executable detail with instructions, so the safest path is to tune examples and interfaces before you rewrite the overall scaffold.
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.