Federated Agentic AI: A Privacy-Preserving Architecture for Collaborative Intelligence Across Distributed Enterprises

Abstract

Enterprises increasingly hold data and operational capability that is more valuable in combination than in isolation a manufacturer and its tier-one suppliers each see only part of a demand shock, a group of regional banks each see only their own slice of a cross-institution fraud ring, a hospital network and its referral partners each hold only a fragment of a patient’s full clinical picture — yet competitive concerns, trade secrecy, and statutes governing data sharing routinely make centralizing that data into one system legally or commercially impossible. At the same time, 2023 has seen the rapid emergence of agentic artificial intelligence: large-language-model-based systems that do not merely answer a single prompt but autonomously plan multi-step tasks, invoke external tools, observe results, and revise their own plans. Almost every agentic AI system described in the literature to date assumes a single organization’s data, tools, and oversight; the moment two or more independently governed enterprises want their agents to collaborate, existing architectures offer no answer that does not either centralize sensitive data or abandon the autonomous, multi-step reasoning that makes agentic AI valuable in the first place. This paper addresses that gap. Synthesizing the federated learning literature, the privacy-preserving computation literature, and the emerging agentic-AI literature, it develops Federated Agentic AI (FAA): a reference architecture in which each enterprise runs its own language-model agent locally over its own data and tools, and cross-enterprise “collaborative intelligence” occurs only through a privacy-preserving exchange layer secure-aggregated model updates, differentially private statistics, or sanitized intermediate task representations that never transmits raw records and never grants one enterprise’s agent direct access to another’s systems. Because no production deployment of such a system exists to evaluate, this paper adopts a design-science approach: the architecture is built from and evaluated against peer-reviewed findings, and every quantitative comparison presented two comparative tables and four figures spanning capability coverage, privacy-utility trade-offs, coordination overhead, and task-success-rate distributions is explicitly labeled as literature-grounded estimate rather than a measured result. The paper’s contribution is a synthesis of three otherwise separate literatures into one architecture, an explicit mapping of collaborative-intelligence objectives onto specific privacy-preserving and agentic techniques, and a candid account of the coordination-reliability and privacy-leakage limitations any adopter must weigh before deployment.