Source: SiliconANGLE
Most AI governance frameworks focus on training, deployment, and monitoring of large models, but skip the critical step of actually evaluating whether autonomous agents will behave as intended before release—a gap that becomes dangerous as agents gain real-world decision-making power over finance, supply chains, and infrastructure. The governance industry has borrowed audit and compliance playbooks from finance and medicine, but those frameworks assume human-in-the-loop correction; agentic systems need upstream eval engineering to catch failure modes in sandbox environments, not downstream incident response. Companies building agent evaluation infrastructure—synthetic testing, adversarial probing, long-horizon sim validation—are becoming infrastructure-critical for the entire sector, yet most enterprises still treat evals as a footnote to model release rather than a distinct governance discipline.