From Cloud-Native to Trust-Native: A Protocol for Verifiable Multi-Agent Systems

TL;DR

TrustTrack is a protocol that embeds verifiable guarantees into autonomous agents powered by large language models, enabling trust-native operation in high-stakes, regulated domains.

Contribution

It introduces TrustTrack, a novel protocol for embedding structural guarantees like identity and tamper-resistant logs into agent infrastructure, advancing trust-native autonomy.

Findings

TrustTrack enables verifiable identity and policy commitments.

The protocol supports tamper-resistant behavioral logs.

Application in regulated domains demonstrates practical utility.

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&D, legal automation, and AI-native collaboration. We argue that the Cloud -> AI -> Agent -> Trust transition represents the next architectural layer for autonomous systems.