Your question is Long-Term Planning and State. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
How do you handle long-term planning and state management in complex, multi-step agent workflows?
Treat this as a practical machine learning design and implementation task. Using the supplied workflow traces, build a model that predicts the next workflow state while preserving relevant history, handling incomplete observations, and avoiding temporal leakage. Explain how the representation supports planning over long horizons, how you would evaluate state-prediction quality, and how the system should respond to uncertainty or distribution shift.
