New Framework Proposes Grounded Continuous Evaluation for Agentic AI Systems

A new research paper argues that current evaluation methods for large language models are structurally inadequate for assessing deployed, agentic systems. The authors identify four systematic failures: distributional invalidity, where evaluation inputs don't reflect real interaction patterns; temporal invalidity, with evaluations being post-hoc rather than integrated into training; scope invalidity, measuring single-turn outputs instead of long-horizon trajectories; and process invalidity, assessing outputs rather than reasoning processes. These problems become particularly critical in reinforcement learning from human feedback, where reward models are evaluated under conditions that don't match RL training environments, making reward hacking a predictable outcome of flawed evaluation design rather than a training pathology. To address these issues, the researchers propose the Grounded Continuous Evaluation framework and introduce ISOPro, a simulation-based system for fine-tuning and evaluation that replaces learned reward models with more robust alternatives. The paper was published on arXiv with identifier 2604.17573v1, marking it as new research in the field of AI evaluation methodologies.