The proliferation of autonomous software agents necessitates rigorous frameworks for establishing secure and verifiable agent-to-agent (A2A) interactions, particularly when such agents are instantiated as non-human identities(NHIs). We extend the A2A paradigm [1 , 2] by introducing a cryptographically grounded mechanism for lineage verification, wherein the provenance and evolution of NHIs are anchored in append-only Merkle tree structures modeled after Certificate Transparency (CT) logs. Unlike traditional A2A models that primarily secure point-to-point interactions, our approach enables both agents and external verifiers to cryptographically validate multi-hop provenance, thereby ensuring the integrity of the entire call chain. A federated proof server acts as an auditor across one or more Merkle logs, aggregating inclusion proofs and consistency checks into compact, signed attestations that external parties can verify without access to the full execution trace. In parallel, we augment the A2A agent card to incorporate explicit identity verification primitives, enabling both peer agents and human approvers to authenticate the legitimacy of NHI representations in a standardized manner. Together, these contributions establish a cohesive model that integrates identity attestation, lineage verification, and independent proof auditing, thereby advancing the security posture of inter-agent ecosystems and providing a foundation for robust governance of NHIs in regulated environments such as FedRAMP.

This paper presents a formalised architecture for synthetic agents designed to retain immutable memory, verifiable reasoning, and constrained epistemic growth. Traditional AI systems rely on mutable, opaque statistical models prone to epistemic drift and historical revisionism. In contrast, we introduce the concept of the Merkle Automaton, a cryptographically anchored, deterministic computational framework that integrates formal automata theory with blockchain-based commitments. Each agent transition, memory fragment, and reasoning step is committed within a Merkle structure rooted on-chain, rendering it non-repudiable and auditably permanent. To ensure selective access and confidentiality, we derive symmetric encryption keys from ECDH exchanges contextualised by hierarchical privilege lattices. This enforces cryptographic access control over append-only DAG-structured knowledge graphs. Reasoning is constrained by formal logic systems and verified through deterministic traversal of policy-encoded structures. Updates are non-destructive and historied, preserving epistemic lineage without catastrophic forgetting. Zero-knowledge proofs facilitate verifiable, privacy-preserving inclusion attestations. Collectively, this architecture reframes memory not as a cache but as a ledger - one whose contents are enforced by protocol, bound by cryptography, and constrained by formal logic. The result is not an intelligent agent that mimics thought, but an epistemic entity whose outputs are provably derived, temporally anchored, and impervious to post hoc revision. This design lays foundational groundwork for legal, economic, and high-assurance computational systems that require provable memory, unforgeable provenance, and structural truth.

One of today's prime trends – blockchain technology – has the remarkable potential to empower other mega-trend, artificial intelligence. Regardless of the ongoing Facebook confidentiality scandal, private companies including Facebook, Baidu, Google and Alibaba are continuing to collect and store a surprising amount of consumer data. Increasingly, data analysts are employing AI algorithms and tools to mine these data-sets – known as big data. However, the Internet of Things (IoT) with autonomous cars and Wi-Fi enabled devices will create enormous challenges regarding storage of data. Google's self-driving vehicle generates roughly 1 GB of data per second, how will current systems cope in a world with every autonomous vehicle sending an approximate 2 Petabytes of data to the cloud per annum?

One of today's prime trends – blockchain technology – has the remarkable potential to empower other mega-trend, artificial intelligence. Regardless of the ongoing Facebook confidentiality scandal, private companies including Facebook, Baidu, Google and Alibaba are continuing to collect and store a surprising amount of consumer data. Increasingly, data analysts are employing AI algorithms and tools to mine these data-sets – known as big data. However, the Internet of Things (IoT) with autonomous cars and Wi-Fi enabled devices will create enormous challenges regarding storage of data. Google's self-driving vehicle generates roughly 1 GB of data per second, how will current systems cope in a world with every autonomous vehicle sending an approximate 2 Petabytes of data to the cloud per annum?

"Lordy, I hope there are tapes," said an exasperated James Comey in his testimony before the Senate Intelligence Committee on June 8. Comey's desire reflects a familiar one for individuals accused of lying when the stakes are high. The former FBI director wished for tapes because, in our society, audio and video recordings serve as a final arbiter of truth. He said, she said always loses to what the tape shows. Greg Allen (@Gregory_C_Allen) is an adjunct fellow at the Center for a New American Security.

"Lordy, I hope there are tapes," said an exasperated James Comey in his testimony before the Senate Intelligence Committee on June 8. Comey's desire reflects a familiar one for individuals accused of lying when the stakes are high. The former FBI director wished for tapes because, in our society, audio and video recordings serve as a final arbiter of truth. He said, she said always loses to what the tape shows. Greg Allen (@Gregory_C_Allen) is an adjunct fellow at the Center for a New American Security.