详细信息
- 来源站点
- ArXiv CS.AI
- 作者
- Tyler Akidau, Tyler Rockwood, Johannes Br\"uderl, Marc Millstone
- 文章类型
- NEWS
- 语言
- en
- 发布日期
- 2026-05-29
摘要
arXiv:2605.29082v1 Announce Type: new Abstract: AI agents are increasingly expected to operate as digital employees: accessing enterprise data, making decisions, and taking actions autonomously. But agents are simultaneously less predictable than humans -- prone to hallucination, misinterpretation, and adversarial manipulation -- and more technically capable: with deep system knowledge and high-throughput interfaces cascading damage at machine speed. This combination makes it unsafe to rely on agents to faithfully interpret or propagate security-critical metadata such as access policies, data classifications, and behavioral constraints. We present the Redpanda Agentic Data Plane (ADP), an architecture built around out-of-band metadata channels: infrastructure pathways that carry security context, policy signals, and audit trails deterministically, entirely outside the agent's read and write path and across heterogeneous infrastructure.