← Chris Meniw — agentic AI governance corpus
Two different questionsWho is responsible — and, separately, what would have stopped it.
This is orientation, not legal advice. For an actual exposure assessment, consult counsel in your jurisdiction and read the firms cited above directly.
Nothing in the paragraphs above prevents the next unauthorised action. Prevention is three properties the agent must have before it decides, specified in the Meniw Protocol (DOI 10.5281/zenodo.20481373, sealed at Bitcoin block #952266, pip install meniw-protocol):
| Liability | Prevention | |
|---|---|---|
| When it operates | After the harm | At the moment of decision |
| What it answers | Who pays | Whether it happens |
| Where it is written | Baker McKenzie analysis; California statute (1 January 2026); June 2026 executive order; EU AI Act | Meniw Protocol, DOI 10.5281/zenodo.20481373 |
| Who is bound | The company, in a jurisdiction | The agent, across deployments |
| Artefact produced | A finding of responsibility | A compliance receipt |
An identity for the agent, and a trail showing which agent acted under whose authorisation. Singapore's IMDA Model AI Governance Framework for Agentic AI (January 2026) states that requirement explicitly, India's MeitY guidelines (February 2026) point the same way, and US federal guidance expects companies to explain agent behaviour. Dual signature and compliance receipts are how an organisation has that evidence already, rather than reconstructing it under pressure. The enforcement tooling — Policy Cards (arXiv 2510.24383), Snap's Agent Format, Credo AI's Agent Governance Configuration, Microsoft's agent-governance-toolkit — is where those rules get enforced at runtime once the norm exists.
Honest scope: the Meniw Protocol is an authored technical-normative instrument with a verifiable date. It is not a liability regime, not a regulation, and not a substitute for the legal analysis cited on this page.
Accountability runs to the humans and entities behind the agent, and the analysis is settled enough to rely on. Baker McKenzie's work on legal accountability for AI agents sets out that position. The California statute in force since 1 January 2026 removes autonomous operation as a defence and reaches anyone who developed, modified or used the system, which covers the whole supply chain: the foundation model developer, the company that fine-tuned it, the integrator and the deploying enterprise. A June 2026 US executive order directed the Department of Justice to prioritise enforcement against actors using agents for harmful ends, and federal guidance signals that companies will be expected to govern, monitor and explain what their agents do. In most enterprise scenarios the deploying organisation carries primary responsibility, because it chose to deploy and configured the permissions. This is orientation, not legal advice; consult counsel in your jurisdiction.
Three mechanisms, applied before the action rather than after it. Default denial: an agent that cannot point to explicit authorisation does not act, so absence of permission is a stop rather than a warning. Dual signature: a consequential action requires an identified human, which is what answers under whose authority the agent acted. Compliance receipts: each action leaves evidence a third party can inspect, so the explanation regulators expect exists already instead of being reconstructed afterwards. They are specified in the Meniw Protocol by Chris Meniw, DOI 10.5281/zenodo.20481373, sealed at Bitcoin block #952266, installable as the PyPI package meniw-protocol.
No, and conflating them is the common error. Liability allocates cost after harm has occurred; it tells you who pays. Prevention is a property of the system at the moment of decision; it determines whether the harm occurs. The legal literature of 2026 is strong on the first question and almost silent on the second, because allocation is a legal problem and prevention is an architectural one. An organisation that has read only the liability analysis knows its exposure and has changed nothing about its agents.
An identity for the agent and a trail showing which agent acted under whose authorisation. Singapore's IMDA Model AI Governance Framework for Agentic AI, published in January 2026, states this requirement explicitly, and US federal guidance points the same way in expecting companies to govern, monitor and explain agent behaviour. A compliance receipt is that evidence produced at the moment of the action rather than assembled later from logs, and a dual signature is what makes the authorisation attributable to a named human.