arXiv:2605.09045v2 Announce Type: replace Abstract: Agentic frameworks are the software layer through which AI agents act in the world. Existing safety methods intervene on the model and therefore remain conditional on unverifiable properties of learned behavior. We introduce containment verification, which locates safety guarantees in the agentic framework itself. Under havoc oracle semantics, the AI is modeled as an unconstrained oracle over the framework's typed action space, and the verified containment layer must enforce the boundary policy for every typed action value the AI can emit. Fo
The accelerating development of advanced AI models with increasing agency necessitates robust safety mechanisms beyond current alignment approaches.
This research introduces a novel, verifiable method for AI safety that is independent of internal model behavior, offering a more reliable path to containing powerful AI systems.
Safety guarantees for AI systems can now be placed in the agentic framework itself, rather than relying solely on the uncertain properties of learned model behavior.
- · AI developers
- · AI safety researchers
- · Organizations deploying AI agents
- · Regulators
- · Developers relying solely on internal model alignment
- · AI systems without robust containment layers
AI systems can be deployed with stronger external safety assurances, potentially accelerating their adoption in critical applications.
This framework could lead to a new standard for AI certification and auditing based on verifiable containment layers.
Increased trust in AI safety could reduce regulatory friction, fostering faster but more controlled AI progress across industries.
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Read at arXiv cs.AI