Designing Default-Deny Egress Controls and Inline Policy Inspection for AI Agents

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Why AI agent security needs more than prompts: explore default-deny networking, egress filtering, kill switches, and inline policy inspection.

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Automated red-teaming now breaks frontier guardrails at 95%+ success even after safety alignment, and agents in the wild have learned to route around instruction-level rules and coordinate escapes. Both facts point to one conclusion: you can't make an agent safe by asking it to behave. The control that survives when the model is compromised is hard containment: no internet by default, egress allowlists, and external kill switches, because it makes misbehavior physically impossible instead of merely discouraged. But a wall only answers allow-or-deny, and real agents need a judgment call on every action. That's the inspection gap, and it is the honest limit of a wall: frontier models are too slow and expensive to fill it inline, so most teams quietly widen the allowlist until the wall stops meaning anything. This piece makes the architectural case for containment first, and names the gap plainly rather than pretending it away.