Risks and Controls for Multi-Agent Systems: an analytical framework for deployment of AI agents across organisational boundaries

Alistair Reid; Simon O'Callaghan; Dustin Venini; Liam Carroll; Tiberio Caetano

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arXiv · 2608.26626

Risks and Controls for Multi-Agent Systems: an analytical framework for deployment of AI agents across organisational boundaries

Alistair Reid·Simon O'Callaghan·Dustin Venini·Liam Carroll·Tiberio Caetano

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Abstract

This report presents a framework to help organisations, policymakers and researchers reason about the risks that emerge when AI agents interact with each other, how those risks change as interactions cross organisational boundaries, and the controls that may help address them. As organisations deploy AI agents, those agents will increasingly interact with each other: inside the organisation, with the agents of partners, customers and suppliers, and with unknown counterparties on the open internet. Failures can emerge from the interactions themselves, and once those interactions cross an organisation's perimeter, no single organisation can fully see, control or govern them. The report introduces three deployment tiers, defined by the minimum common governance binding any two interacting agents: singular governance, where one organisation governs every agent; federated governance, where multiple organisations deploy into a shared environment under agreed rules; and open environments, where agents operate with no central authority and shared standards are adopted voluntarily if at all. Within each tier, the report examines risk factors, failure modes and available controls. It identifies who is positioned to apply the controls, and where no actor is positioned to act, it characterises the gap and the collective action required to close it.

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Alistair Reid, Simon O'Callaghan, Dustin Venini, Liam Carroll, Tiberio Caetano. 2026-08-27. Risks and Controls for Multi-Agent Systems: an analytical framework for deployment of AI agents across organisational boundaries. https://arxiv.org/abs/2608.26626

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