Self-Evolving Agents

Self-evolving agents are AI systems that modify their own behavior, prompts, tools, or code over time based on feedback from their environment, rather than staying fixed as released. For governance teams, this matters because the system an organization approved at launch may not be the system running months later, which complicates validation, change control, and accountability. Regulators and internal risk committees generally expect a documented, reviewable version of any deployed system, so self-modifying behavior raises hard questions about how changes are tested, logged, and approved before they reach production.

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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