From Migration to Calibration: Preserving Agent Capabilities across Models, Jurisdictions, and Scale

The paper proposes a framework for agent calibration that ensures consistent capabilities when agents are deployed, migrated, or scaled across different models, harnesses, infrastructures, and user contexts. It defines standards for basic capabilities, technical environments, and user contexts, diagnoses gaps, applies revisions, and rechecks within fixed budgets, using diagnostic source behavior and mandatory tests to qualify agents. The framework includes semantic checkpoints, repair localization, and reusable configuration artifacts, and outlines an automatic calibration tool that relies on limited authorized user trajectories and tests, though empirical validation remains pending.

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• agents
• reinforcement-learning