{"ID":2890322,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.22077","arxiv_id":"2507.22077","title":"From Cloud-Native to Trust-Native: A Protocol for Verifiable Multi-Agent Systems","abstract":"As autonomous agents powered by large language models (LLMs) proliferate in high-stakes domains -- from pharmaceuticals to legal workflows -- the challenge is no longer just intelligence, but verifiability. We introduce TrustTrack, a protocol that embeds structural guarantees -- verifiable identity, policy commitments, and tamper-resistant behavioral logs -- directly into agent infrastructure. This enables a new systems paradigm: trust-native autonomy. By treating compliance as a design constraint rather than post-hoc oversight, TrustTrack reframes how intelligent agents operate across organizations and jurisdictions. We present the protocol design, system requirements, and use cases in regulated domains such as pharmaceutical R\u0026D, legal automation, and AI-native collaboration. We argue that the Cloud -\u003e AI -\u003e Agent -\u003e Trust transition represents the next architectural layer for autonomous systems.","short_abstract":"As autonomous agents powered by large language models (LLMs) proliferate in high-stakes domains -- from pharmaceuticals to legal workflows -- the challenge is no longer just intelligence, but verifiability. We introduce TrustTrack, a protocol that embeds structural guarantees -- verifiable identity, policy commitments,...","url_abs":"https://arxiv.org/abs/2507.22077","url_pdf":"https://arxiv.org/pdf/2507.22077v1","authors":"[\"Muyang Li\"]","published":"2025-07-25T04:38:38Z","proceeding":"cs.MA","tasks":"[\"cs.MA\",\"cs.AI\",\"cs.CR\"]","methods":"[\"Large Language Model\",\"Language Model\",\"Generative Adversarial Network\"]","has_code":false}