Tag
The article argues that AI agent safety rules should be implemented as hard workflow constraints and permissions rather than relying solely on prompt instructions. It emphasizes the need for explicit checks, approvals, and logs for sensitive or irreversible actions.
The author argues that current AI agent safety measures like guardrails and monitoring are insufficient, proposing 'external admission' as a stricter pattern where execution authority is withheld until an external authority explicitly allows high-impact actions.
This paper introduces 'authorization propagation' as a distinct security challenge in multi-agent AI systems, arguing that identity governance must be treated as infrastructure to maintain authorization invariants across autonomous agent interactions.
This paper introduces Partial-Evidence-Bench, a deterministic benchmark for measuring 'authorization-limited evidence' failures in agentic AI systems. It evaluates how models handle tasks where access control restricts visibility, assessing their ability to recognize and report incomplete information rather than silently producing seemingly complete but incomplete answers.
OpenAI introduced a hybrid real-time access engine combining rate limits and pay-as-you-go credits for Codex and Sora, enabling users to seamlessly exceed rate limits by spending credits while maintaining system fairness and performance.
OpenAI introduces enterprise-grade features for API customers including Private Link, Multi-Factor Authentication, Projects for granular control, and significant Assistants API improvements with enhanced file retrieval (500x increase), streaming support, and fine-tuned model support.
OpenAI announces the release of an API for accessing its AI models with a general-purpose text interface, launching in private beta with strict safety measures including mandatory production reviews and content restrictions to prevent harmful use cases.