The Sim-to-Real Gap of Foundation Model Agents: A Unified MDP Perspective

Researchers propose formalizing the evaluation of foundation model agents through a classical sim-to-real framework based on Markov Decision Processes, addressing the gap between simulated training and real-world deployment. The work advocates adopting established robotics solutions like domain randomization and establishing standardized benchmarks to build more reliable AI agents for production applications.

Foundation model agents represent a frontier in AI deployment, yet they encounter a critical challenge that robotics engineers have grappled with for decades: the sim-to-real gap. This research reframes agent robustness problems through the lens of classical control theory, identifying misalignment across four MDP components—observation, action, transition, and reward—that cause failures in production environments. The approach is significant because it bridges two historically separate research communities, preventing the foundation model field from reinventing solutions already validated in robotics.

The paper's multilingual tool-calling example illustrates how severe observation space gaps manifest as operationally invalid actions despite semantically correct model outputs. This distinction matters: an AI system might correctly interpret user intent but fail to execute it properly due to environmental constraints or specification mismatches. This problem intensifies as foundation models scale across diverse deployment contexts with varying physical, semantic, and operational properties.

For developers and organizations deploying AI agents, this research provides a diagnostic framework to identify failure modes before production deployment. The emphasis on standardized stress test benchmarks addresses a critical gap in the current AI evaluation landscape, where robustness testing remains ad-hoc and non-standardized. This unified vocabulary enables cross-domain knowledge transfer from robotics into AI systems engineering.

The long-term implication centers on trustworthiness. By systematizing sim-to-real evaluation, the field can establish confidence levels for agent deployment across different risk domains. Organizations will increasingly demand such standardized assurance measures before integrating foundation model agents into critical workflows, making this research agenda foundational for the next generation of production-ready AI systems.

• →Foundation model agents face sim-to-real gaps that can be systematically analyzed using the four MDP elements: observation, action, transition, and reward.
• →Established robotics solutions like domain randomization offer proven methods to bridge sim-to-real gaps in AI agent deployment.
• →Observation space misalignment can produce semantically correct but operationally invalid actions, as demonstrated through multilingual tool-calling examples.
• →Standardized stress test benchmarks are essential for evaluating agent robustness before real-world deployment.
• →Adopting a unified vocabulary from control theory enables better knowledge transfer and faster development of trustworthy AI agents.