The Containment Gap: How Deployed Agentic AI Frameworks Fail Public-Facing Safety Requirements

📰 ArXiv cs.AI

Learn how deployed agentic AI frameworks fail to meet public-facing safety requirements and how to address the containment gap

advanced Published 12 Jun 2026
Action Steps
  1. Apply containment principles to agentic AI frameworks using compositional models
  2. Analyze existing frameworks for architectural-level structural safety guarantees
  3. Identify gaps in safety requirements for public-facing domains
  4. Develop and implement new safety protocols to address the containment gap
  5. Test and evaluate the effectiveness of new safety protocols
  6. Refine and iterate on safety protocols based on feedback and results
Who Needs to Know This

AI engineers, researchers, and developers working on agentic AI frameworks will benefit from understanding the containment gap and its implications for safety guarantees

Key Insight

💡 Current agentic AI frameworks lack architectural-level structural safety guarantees, putting public-facing domains at risk

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🚨 Containment gap in agentic AI frameworks poses safety risks in public-facing domains 🚨

Key Takeaways

Learn how deployed agentic AI frameworks fail to meet public-facing safety requirements and how to address the containment gap

Full Article

Title: The Containment Gap: How Deployed Agentic AI Frameworks Fail Public-Facing Safety Requirements

Abstract:
arXiv:2606.12797v1 Announce Type: new Abstract: Agentic large language model systems that autonomously invoke tools, maintain persistent memory, and execute multi-step plans are increasingly deployed in public-facing domains, including government services, healthcare triage, and financial advising. We ask whether the frameworks used to build these systems provide architectural-level structural safety guarantees. Applying six containment principles derived from a compositional model of agentic ar
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