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Design LLM Agent Architecture for Digital Twin Testing

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Linked challenge: Hyper-realistic Humanoid Digital Twin for Autonomous Task Validation with LLM Agents

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Linked challenge
Hyper-realistic Humanoid Digital Twin for Autonomous Task Validation with LLM Agents

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Design the architecture for your LLM agent using Smolagents. Detail how it will interface with a simulated humanoid robot within a digital twin environment (e.g., via a ROS bridge or direct API calls to a physics engine). Specify the prompt engineering strategy for OpenAI o4-mini to generate diverse and challenging test scenarios, including corner cases for robot failure. What data structures will be used to represent robot state, environmental conditions, and task goals for the agent's internal reasoning?

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Preserve the role framing, objective, and reporting structure so comparison runs stay coherent.

Tune next

Swap in your own domain constraints, anomaly thresholds, and examples before you branch variants.

Verify after

Check whether the prompt asks for the right evidence, confidence signal, and escalation path.