A new paper proposes addressing the sim-to-real gap in foundation model agents by applying classical Markov Decision Process frameworks, aiming to enhance their robustness in real-world applications. The research advocates for established solutions like domain randomization to improve agent reliability and standardize evaluation benchmarks.