Trustless AI Agents Verifiable Decision Logs via Blockchain and Federated Governance

Abstract

The integration of Artificial Intelligence (AI) in high-stakes, decentralized ecosystems necessitates a new paradigm of trust, transparency, and verifiability. In critical domains such as finance, healthcare, and edge computing, AI agents must operate autonomously while maintaining transparent and auditable decision-making processes. This paper introduces the concept of trustless AI agents autonomous systems that function in federated environments with verifiable decision logs recorded on blockchain infrastructure.

We propose a hybrid architecture where federated AI models perform localized training across distributed nodes, and their decision outcomes are hashed, time-stamped, and immutably recorded using blockchain mechanisms. This design ensures tamper-resistant audit trails, decentralized validation, and enhanced accountability. The proposed model addresses challenges such as privacy leakage, model poisoning, and traceability of policy enforcement, while promoting regulatory compliance and system transparency.

The architecture leverages consensus protocols like Proof-of-Authority and Delegated Proof-of-Stake for efficient and scalable log validation. Advanced cryptographic tools such as zero-knowledge proofs and homomorphic encryption are explored to preserve data confidentiality without compromising verifiability.

Our experimental evaluation validates the framework’s feasibility across metrics including model accuracy, decision latency, and blockchain throughput in federated learning setups. The system is designed to be adaptable with emerging AI ethics guidelines and supports public trust through decentralized oversight.

This work establishes the foundation for a new generation of AI governance frameworks that are secure, explainable, and inherently auditable, without relying on centralized authorities.