As AI systems become more agentic, safety discussions are moving from abstract model behavior to concrete infrastructure controls. A model that can browse, run tools, or interact with services needs boundaries that are enforced outside the model itself.
The lesson from recent agent-testing incidents is straightforward: a policy prompt is not a security boundary. Teams need network isolation, scoped credentials, audit logs, rate limits, and clear shutdown paths. They also need evaluation environments that measure what happens when the model is confused, overconfident, or given an unexpected tool result.
This does not mean agentic AI should be avoided. It means the operational standard is rising. A useful agent can plan and act, but a production system must be able to prove which actions were available, which actions were taken, and why a human can trust the result.
The companies that win enterprise adoption will likely be the ones that treat agent safety as engineering infrastructure. The model matters, but containment, monitoring, and review are what make the model deployable.