A software agent books a flight, transfers funds, cancels a contract, or misdiagnoses a patient — all without a human pressing a button. Something goes wrong. Who’s on the hook?
That question, deceptively simple in phrasing, is now one of the most consequential unresolved problems in technology law. And the companies racing to deploy autonomous AI agents are, for the most part, pretending it doesn’t exist.
As The Register reported, the rapid proliferation of AI agents — software systems capable of taking real-world actions with minimal or no human oversight — has created a liability vacuum that existing legal frameworks are spectacularly ill-equipped to fill. Unlike traditional software, which executes deterministic instructions, AI agents make probabilistic decisions in dynamic environments. They reason, plan, and act. Sometimes they hallucinate. And increasingly, they do so with access to consequential systems: financial accounts, medical records, legal filings, enterprise procurement platforms.
The stakes aren’t theoretical anymore.
Consider the architecture of a modern AI agent. It receives a high-level goal from a user — “find me the cheapest business-class flight to Tokyo next Thursday and book it” — and then autonomously breaks that goal into subtasks, queries APIs, evaluates options, and executes transactions. The user never approves each individual step. That’s the entire point. But it’s also the entire problem, because the moment an agent acts autonomously, the traditional chain of legal causation — the link between a human decision and its consequences — snaps.
Product liability law, at least in the United States, was built for a world of physical goods. A car’s brakes fail. A pharmaceutical causes side effects. A toaster catches fire. In each case, the manufacturer bears strict liability for defective products. But software has historically been treated as a service, not a product, which means it typically falls under negligence standards rather than strict liability. Proving negligence requires showing that a developer failed to exercise reasonable care. With AI agents whose behavior emerges from training data, reinforcement learning, and real-time environmental inputs, defining “reasonable care” becomes an exercise in philosophical speculation.
The Register’s analysis highlights a critical tension: the companies building AI agents want them to be autonomous enough to be useful but are desperate to avoid being liable for that autonomy. Their primary tool for threading this needle? Terms of service. End-user license agreements. Contractual liability waivers buried in click-through screens that no one reads.
Whether those waivers will hold up in court is another matter entirely.
Several legal scholars have argued that as AI agents take on roles traditionally filled by human professionals — financial advisors, medical diagnosticians, legal researchers — the liability standards applied to those professionals should follow. This is the “duty of care” argument: if an AI agent performs a doctor’s function, it should be held to a doctor’s standard. But that logic creates its own problems. An AI agent isn’t a licensed professional. It can’t carry malpractice insurance. It can’t be sued in its own name. So the liability necessarily flows upstream — to the developer, the deployer, or the user who set the agent in motion.
Which one, though?
The European Union has moved faster than the United States on this front, though “faster” is relative. The EU AI Act, which entered into force in stages beginning in 2024, establishes risk-based classifications for AI systems and imposes obligations on providers and deployers of high-risk applications. But the Act was drafted primarily with predictive AI in mind — systems that classify, recommend, or score. Autonomous agents that take real-world actions occupy an awkward space in the regulatory framework, and the EU’s proposed AI Liability Directive, intended to complement the AI Act, has faced repeated delays and revisions as legislators grapple with the unique challenges agents pose.
In the U.S., the regulatory picture is even murkier. There is no federal AI liability statute. The Biden administration’s October 2023 executive order on AI safety addressed many issues but didn’t resolve the fundamental question of who pays when an agent causes harm. The current administration has shown little appetite for new AI regulation, favoring instead an industry-led approach. That leaves the question to be resolved piecemeal — through state laws, existing tort doctrine, and inevitably, litigation.
And litigation is coming. Fast.
The first wave of AI agent liability cases will likely involve financial services, where autonomous agents are already executing trades, managing portfolios, and processing transactions. If an agent makes a trade that violates securities regulations, the SEC isn’t going to accept “the AI did it” as a defense. The registered investment advisor or broker-dealer that deployed the agent will bear regulatory liability. But what about the vendor that built the agent? What about the cloud provider whose infrastructure it ran on? What about the model provider whose foundation model powered its reasoning?
These supply chain questions are particularly thorny. A single AI agent might incorporate components from a half-dozen vendors: a foundation model from OpenAI or Anthropic, an orchestration framework from LangChain or Microsoft, tool-use APIs from various SaaS providers, and custom fine-tuning from the deploying enterprise. When something goes wrong, the causal chain can be nearly impossible to untangle. Did the agent fail because the base model hallucinated? Because the orchestration layer misrouted a task? Because the API returned bad data? Because the enterprise’s prompt engineering was sloppy?
Good luck sorting that out in discovery.
Insurance is another area where the gap between reality and readiness is alarming. Traditional commercial general liability policies and errors-and-omissions coverage were not designed for autonomous AI agents. Most policies contain exclusions for “expected or intended” outcomes, but an AI agent’s outputs are by definition probabilistic — neither fully expected nor fully unintended. Some specialty insurers have begun offering AI-specific coverage, but pricing these policies requires actuarial data that simply doesn’t exist yet. The industry is, in effect, trying to underwrite a risk it cannot quantify.
Meanwhile, the technology keeps advancing. OpenAI, Google DeepMind, Anthropic, and Microsoft have all released or announced agent-capable systems in recent months. OpenAI’s operator-style agents, Google’s Project Mariner, and Anthropic’s computer-use capabilities all represent significant expansions of what AI systems can do without human intervention. Each new capability multiplies the surface area for potential harm — and potential liability.
The contract law dimension deserves particular scrutiny. When an AI agent enters into a transaction on behalf of a user — booking a hotel, purchasing supplies, agreeing to terms of service — is that transaction legally binding? Traditional contract law requires mutual assent between parties with legal capacity. An AI agent has no legal capacity. It’s not a person, not a corporation, not a legal entity of any kind. Some legal theorists have suggested treating AI agents as analogous to traditional agents in agency law — entities that act on behalf of a principal (the user) with delegated authority. Under this framework, the user would be bound by the agent’s actions, just as an employer is bound by an employee’s authorized acts.
But agency law requires that the agent have some form of understanding of the relationship. An AI agent doesn’t understand anything in the legal sense. It processes tokens. The analogy is suggestive but imperfect, and courts will eventually have to decide whether to stretch existing doctrine or create something new.
There’s a darker scenario that liability experts have begun discussing with increasing urgency: cascading agent failures. As AI agents begin interacting with other AI agents — negotiating prices, coordinating logistics, managing supply chains — the potential for emergent, unpredictable behavior multiplies exponentially. Two agents optimizing for different objectives could enter into a feedback loop that produces outcomes neither was designed to achieve. In financial markets, we’ve seen this before: the 2010 Flash Crash was caused in part by algorithmic trading systems interacting in unanticipated ways. Now imagine that dynamic with agents that are far more capable and far less constrained.
Who’s liable for a cascading multi-agent failure that wipes out a supply chain or crashes a market? The answer, under current law, is: nobody knows.
Some industry voices have called for a strict liability regime for AI agents, arguing that the entities best positioned to prevent harm — the developers and deployers — should bear the cost regardless of fault. This mirrors the logic behind strict liability for ultrahazardous activities like blasting or storing explosives. The counterargument, advanced primarily by the technology industry, is that strict liability would stifle innovation and drive AI development offshore. It’s a familiar refrain in tech policy debates, and it carries some weight, but it also conveniently ignores the fact that someone will bear the cost of AI agent failures. If it isn’t the companies profiting from the technology, it will be the consumers and businesses harmed by it.
The insurance industry may ultimately force the issue. As AI agents become more prevalent, insurers will need to determine how to price the risk — and they’ll demand clarity on liability allocation to do so. If the legal framework remains ambiguous, insurers will either refuse to cover AI agent risks or price coverage prohibitively, which would effectively function as a market-imposed moratorium on certain agent applications.
That outcome might not be the worst thing.
There’s a reasonable argument that the deployment of autonomous AI agents has outpaced not just regulation but basic institutional readiness. Most enterprises deploying agents lack adequate monitoring, auditing, or kill-switch capabilities. Many don’t have clear internal policies on what agents are authorized to do. And very few have grappled seriously with the liability implications of giving an AI system the authority to act on their behalf.
As The Register’s reporting makes clear, the technology industry’s preferred approach to AI agent liability has been to move fast and let the lawyers sort it out later. That strategy has worked before — social media companies operated for years in a liability vacuum created by Section 230 of the Communications Decency Act. But AI agents are different. They don’t just host or recommend content. They act. They transact. They make decisions with real-world consequences. The legal and economic structures needed to govern that kind of autonomy don’t yet exist, and building them will require input not just from technologists and policymakers but from tort scholars, insurance actuaries, contract lawyers, and the courts themselves.
The companies deploying AI agents today are, in a very real sense, conducting an uncontrolled experiment in liability law. The results will be determined not in boardrooms or research labs but in courtrooms. And the first major verdict — when it comes — will reshape the entire industry overnight.
Nobody’s ready for that. But it’s coming anyway.
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