Governing Autonomous AI Agents in Complex Digital Societies: A Mesh-Based Framework for Trust, Privacy, and Accountability

Yisong Chen; Chuanhao Nie; Yexin Tian

doi:10.63646/UBPX2524

Authors

Yisong Chen¹ https://orcid.org/0009-0009-4978-7565
- Conceptualization
- Formal Analysis
- Investigation
- Methodology
- Writing – Original Draft Preparation
Chuanhao Nie¹ https://orcid.org/0009-0006-4208-6414
- Data Curation
- Software
- Validation
- Visualization
Yexin Tian^1,* https://orcid.org/0009-0000-7873-6681
- Conceptualization
- Project Administration
- Supervision
- Writing – Review & Editing

¹ College of Computing, Georgia Institute of Technology, Atlanta, GA, USA 30332

Corresponding author: yexintian@gatech.edu

DOI:

https://doi.org/10.63646/UBPX2524

Keywords:

Agentic AI; Mesh Architecture; Trust Frameworks; Privacy Preservation; Algorithmic Accountability; Federated Learning; Large Language Models; Digital Governance

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.

How to Cite

Chen, Y., Nie, C., & Tian, Y. (2026). Governing Autonomous AI Agents in Complex Digital Societies: A Mesh-Based Framework for Trust, Privacy, and Accountability. Journal of Technology Innovation and Society, 2(1), 35-52. https://doi.org/10.63646/UBPX2524

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