Acting with AI: An Interaction-Based Framework for Agentic Tort Liability
For legal scholars and policymakers, it provides a structured approach to allocate responsibility for harms caused by autonomous AI agents, addressing a gap in current tort law.
The paper proposes a legal framework for assigning tort liability when agentic AI systems cause harm, drawing on Bratman's planning theory and common law doctrines. It distinguishes three interaction types (autonomous drift, pure tool use, collaborative planning) and maps each to existing liability doctrines, using the interaction log as evidence.
Agentic AI systems can plan over multiple steps, use tools, and execute tasks over time. When such systems cause harm, tort law struggles to allocate responsibility because the harmful path may be neither fully chosen by the user nor specifically foreseen by the developer. This paper proposes an interaction-based framework for agentic torts, drawing on Michael Bratman's planning theory and on the common law's treatment of human-human concerted action. We distinguish three interaction types: autonomous drift, pure tool use, and collaborative planning. Pure tool cases remain governed by ordinary product-defect and warning doctrines; collaborative planning cases map onto the independent contractor control test, professional malpractice, and negligent misrepresentation; autonomous drift maps onto frolic and detour under respondeat superior and strict product liability. The framework treats the stateful interaction log as the primary evidentiary trace, allowing courts to infer where the human-AI trajectory departed from the authorized undertaking and where liability should attach. We resolve four incident-anchored cases, situate the account alongside strict-liability and insurance-based proposals, note its relationship to regulatory oversight, and propose a ``Reasonable Agent'' standard built around constraint verification, epistemic transparency, runtime grounding, and forensic logging.