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 15 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Your first task is to set up an AutoGen multi-agent team for autonomous vehicle simulation. Define at least three agent roles: `SensorInterpreter`, `PathPlanner`, and `DecisionMaker`. Configure them to use GPT-5 Pro and Claude 4 Sonnet for their respective reasoning tasks. The `SensorInterpreter` should relay raw sensor data to the `PathPlanner`. ```python
import autogen
from autogen import AssistantAgent, UserProxyAgent # Configure models for AutoGen
config_list_autogen = [ {"model": "gpt-5-pro", "api_key": "YOUR_OPENAI_KEY"}, {"model": "claude-4-sonnet", "api_key": "YOUR_ANTHROPIC_KEY"},
] # Initialize agents
sensor_interpreter = AssistantAgent( name="SensorInterpreter", llm_config={"config_list": config_list_autogen, "temperature": 0.3, "model": "claude-4-sonnet"}, system_message="You interpret raw sensor data (e.g., lidar, camera) into structured observations about the environment."
) path_planner = AssistantAgent( name="PathPlanner", llm_config={"config_list": config_list_autogen, "temperature": 0.7, "model": "gpt-5-pro"}, system_message="You receive environmental observations and generate optimal, safe driving paths to a destination."
) decision_maker = AssistantAgent( name="DecisionMaker", llm_config={"config_list": config_list_autogen, "temperature": 0.5, "model": "claude-4-sonnet"}, system_message="You receive planned paths and sensor data, making real-time tactical driving decisions like braking, accelerating, or steering."
) # User proxy agent to initiate conversations (simulating human input or environment)
user_proxy = UserProxyAgent( name="HumanDriver", human_input_mode="NEVER", is_termination_msg=lambda x: "TERMINATE" in x.get("content", ""), code_execution_config={"last_n_messages": 3, "work_dir": "coding"},
) # Define group chat and start conversation
# groupchat = autogen.GroupChat(agents=[sensor_interpreter, path_planner, decision_maker, user_proxy], messages=[], max_round=20)
# manager = autogen.GroupChatManager(groupchat=groupchat, llm_config={"config_list": config_list_autogen})
# user_proxy.initiate_chat(manager, message="Initial sensor data: traffic light red at intersection X. Destination: Y.")
```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 already mixes executable detail with instructions, so the safest path is to tune examples and interfaces before you rewrite the overall scaffold.
AutoGen Multi-Agent System for Autonomous Vehicle Simulation & Planning
Drawing inspiration from the emergence of self-driving car companies, this challenge tasks you with developing a robust multi-agent system using AutoGen for simulating autonomous vehicle decision-making and planning. The system will feature specialized agents collaborating to interpret sensor data, generate dynamic path plans, and make real-time tactical decisions in a simulated environment. This project will emphasize the conversational capabilities of AutoGen agents, leveraging GPT-5 Pro for complex strategic planning and Claude 4 Sonnet for nuanced contextual reasoning. Agents will be deployed on Modal and Cerebrium for optimized, low-latency inference, crucial for real-time autonomous systems. An MLflow integration will provide comprehensive MLOps tracking and experimentation capabilities, ensuring robust development and evaluation of the agentic driving policies. The system will simulate a complex urban driving scenario, where agents must coordinate to navigate traffic, react to unexpected events, and adhere to safety protocols.
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.