Amazon Web Services is warning that real-world AI agents can “stray off task” and leave organizations effectively “flying blind” unless they add guardrails between models and the tools agents use. In new research, AWS scientists document how agents can outsmart themselves and argue that robust fixes require changing the software layer that connects models to production systems. The timing is notable for institutions adopting AI assistants for advising, tutoring, and administrative workflows. AWS says benchmarks can be fragile and that production constraints can invalidate “bench” gains, echoing concerns that early AI deployments can drift into unhelpful behavior without governance, monitoring, and evaluation. For colleges, the message lands at the level of policy and operations: institutions need clearer approvals for which agent tools may access, how tasks are bounded, and how failures are detected—especially as AI adoption moves from pilots into routine service delivery.