Continuous Evaluation

Continuous evaluation is the practice of repeatedly testing an AI system after deployment, rather than only before launch, to check that its outputs remain accurate, safe, and fair as data, users, and underlying models change. For compliance and risk teams, it turns a one-time approval into an ongoing record of performance, which regulators and auditors increasingly expect for high-risk systems. Gaps in monitoring can leave an organization unaware that a model has drifted into producing biased, inaccurate, or non-compliant results.

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ResearchGlobal2026-11-01

Self-Evolving AI Agents Break the Assumption Underlying Every Change Control

A survey published on arXiv examines safety risks from AI agents that can modify their own behavior during operation. The research identifies four core hazards: loss of control, unsafe adaptation, difficulty evaluating the modified system, and new security threats introduced by self-modification. Enterprises face a structural governance gap because standard change management and version-control processes assume a human initiates every change to a deployed AI system.

agentic AI self-evolving agents agent safety change management continuous evaluation human oversight