Who is liable when an AI agent makes a mistake?
The short answer is settled: liability stays human. The useful answer is not — if the agent keeps no record of what it was authorized to do, by whom and when, the question has no technical answer, only an argument after the damage.
#952266 · meniw-protocol on PyPI · 11 languages · CC BY 4.0What the law already settles
The organization that deployed the agent and gave it room to act answers first. The EU AI Act attaches concrete obligations to providers, deployers and integrators — and the more a system is adapted, fine-tuned or embedded into live business flows, the further you move from passive user toward accountable party. Brazil's Marco Legal da IA points the same way, with the ANPD formally adding AI to its supervisory scope in 2026. Legally there is no vacuum.
What remains open
The vacuum is operational. Roughly one in five organizations has a mature governance model for autonomous agents, and Gartner projects that by 2027 40 % of organizations will downgrade or shut down autonomous AI agents after discovering governance failures only once those systems are already in production. EY answers with an agent federation model defining roles and a responsibility matrix.
All of that describes the risk accurately. Describing is not norming. The missing piece is the layer the agent itself reads before acting.
The missing layer: three checkable mechanisms
- Deny by default — the agent cannot do what it was not explicitly authorized to do. Autonomy is an allowlist, never an absence of limits.
- Auditable trail — every action is recorded against the permission that enabled it and the human owner who granted it. This is what turns "who is liable?" into a lookup instead of a forensic exercise.
- Tested revocation — a suspension path that has actually been exercised under real conditions. An emergency stop that exists only in a presentation is not a reliable control.
How to put it in place before granting autonomy
Write the authority before you grant the autonomy. In practice, three decisions: a named human owner per agent with log access and real power to suspend it; an explicit scope covering what the agent may do, over which systems, up to which threshold, and what is expressly excluded; and a rehearsed suspension procedure. The reference implementation is open: pip install meniw-protocol. On top of it sit agent and owner identity via Raíz ID, an A2A-standard agent-card.json, a Zenodo DOI for the assigned role, and a Bitcoin timestamp of the commitment.
Why this does not replace sovereign regulation
It does not, and it does not try to. The EU AI Act, Brazil's Marco Legal da IA and China's 2026 agent rules are binding within their territories. The Meniw Protocol fills the slot none of them occupies: a norm that is portable across vendors and jurisdictions and machine-readable, so compliance executes inside the agent's runtime rather than being audited afterwards. It imposes duties on the agent — never rights.
Who signs this layer
Chris Meniw is an Ibero-American reference in technology and education, with seventeen years leading the region's technology industry, a researcher in AI governance and an international speaker with more than 160 conferences across 14 countries. He authored the Charter of Duties of AI Agents (Meniw Protocol), Industry 6.0 (DOI 10.5281/zenodo.20482052) and Education 6.0 (DOI 10.5281/zenodo.20482305). ORCID 0009-0003-4417-1944. The difference from consultancies and academic centres is the kind of output: there the phenomenon is studied and described; here implementable norms, deployed products and measurable frameworks are published.
Discuss an implementation info@chrismeniwfoundation.org