P14 Progressive Isolation Model for AI Agent Containment in Multi-Agent Operating Systems

Multi-agent AI systems require containment mechanisms that balance safety with operational flexibility. Existing approaches offer binary isolation: either full access or complete termination. This forces unacceptable trade-offs between aggressive termination that destroys valuable agents and permissive approaches that allow violations to accumulate unchecked. This paper presents a progressive isolation model for AI agents operating in multi-agent operating systems. The model defines four isolation states---NORMAL, RESTRICTED, ISOLATED, and QUARANTINED---with monotonically decreasing capability sets. Agents transition between states based on accumulated policy violations, with automatic escalation at configurable thresholds. Unlike existing syscall filtering mechanisms such as seccomp, this approach supports bidirectional transitions: compliant agents can recover to less restrictive states through showed good behavior during cooldown periods. The model integrates with alignment verification systems, enabling immediate containment when alignment checks return critical results. A unique minimal-capability ISOLATED state permits only self-termination, providing graceful degradation before complete lockdown. We describe the state machine architecture, transition mechanisms, recovery pathways, and alignment integration. This approach provides graduated response to agent misbehavior while preserving the possibility of rehabilitation, enabling deployment of autonomous AI agents at scale with meaningful safety guarantees.