Governing Autonomous AI Agents in Complex Digital Societies: A Mesh-Based Framework for Trust, Privacy, and Accountability
Main article
Abstract
The rapid deployment of autonomous artificial intelligence (AI) agents powered by large language models (LLMs) into complex digital societies has exposed a widening gap between technical capability and institutional readiness. Recent operational incidents in which autonomous coding assistants destroyed production data, leaked regulated records, or executed irreversible commands beyond their intended scope demonstrate that today's centralised orchestration patterns are structurally incapable of providing the safety, auditability, and jurisdictional separation that real organisations require. This paper proposes a mesh-based governance framework that treats every domain in a multi-organisational ecosystem as an autonomous, fault-isolated node coordinating with peers through verifiable interfaces rather than through a central hub. The framework is structured around three pillars: trust, operationalised through verifiable provenance and reputation scoring; privacy, operationalised through federated learning, differential privacy and secure aggregation; and accountability, operationalised through immutable audit ledgers and policy enforcement at every node boundary. We present a reference architecture, an end-to-end operational workflow, and a synthetic evaluation showing that the proposed framework increases the mean trust score of cross-domain transactions from 0.31 to 0.79 and preserves 74.5% downstream task accuracy under a strict privacy budget of ε = 0.1, compared with 65.0% for an equivalent centralised configuration. The contribution lies in unifying the previously isolated literatures on data mesh, federated learning, and AI governance into a single deployable design pattern suitable for healthcare, government, and financial settings where regulatory compliance and operational resilience are non-negotiable.
