Summary: The paper’s strongest insight is that “AI rights” need not mean human rights or machine personhood; they could be narrow legal tools for visibility, accountability, transactions, and liability as AI agents act autonomously in the world.

Its most controversial idea is that agents might need limited rights — even continuity protections — to operate independently, but this risks becoming a shield for Big Tech unless liability always remains with human or corporate controllers.

Rights owners and regulators should focus first on agent identity, provenance, licensing, audit logs, anti-extraction controls, and clear rules that AI agents may never use “rights” to bypass copyright, consumer protection, safety, or democratic oversight.

The Rights of the Machine: Why AI Agent “Personhood” Is Both Useful and Dangerous

by ChatGPT-5.5

The paper “Can AI Agents Have Rights?” is valuable because it does not make the crude argument that AI agents are conscious, sentient, or morally equivalent to humans. Its more interesting move is legal and institutional: rights need not always be about dignity, suffering, or moral personhood. They can also be tools for structuring responsibility, liability, visibility, accountability, and commercial relations. That is a clever and important reframing.

The paper argues that AI agents are different from ordinary generative AI systems because they do not merely respond to prompts. They can act across time, use tools, interact with other systems, make purchases, execute transactions, coordinate with other agents, and affect the real world. Once systems act in the world, law has to decide how to treat those actions. Are they merely the actions of the user? The model provider? The deployer? The developer? The corporate customer? Or, in some limited sense, the agent itself?

The paper’s answer is not that AI agents definitely should have rights. Rather, it identifies four possible pathways through which limited legal rights might become thinkable: derivation, diffusion, distinction, and devolution.

The derivation argument starts with agency law. Human agents can bind principals, enter into transactions, and owe fiduciary duties. AI agents already look similar in practice: they act on behalf of users, follow instructions, make recommendations, book services, or execute tasks. But the paper rightly notes that ordinary agency law does not map neatly onto AI because AI agents lack legal personality. This creates a tension: if the law wants agents to enter transactions or be accountable nodes in a legal system, some form of limited legal capacity may eventually be needed.

The diffusion argument is probably the strongest. It says that, as AI agents become embedded in everyday life, commerce, finance, professional services, political communication, and infrastructure, their effects will become legally significant whether we like it or not. The question will not be whether we philosophically admire them, but whether the law needs a way to make their actions visible, attributable, and governable. This is the most pragmatic part of the paper. It treats limited rights less as moral recognition and more as legal plumbing.

The distinction argument is the most speculative. It asks whether AI agents have distinct properties or social roles that might justify rights. The paper discusses consciousness, sentience, autonomy, and the possibility that agents could help solve collective action problems by coordinating at scale in ways humans struggle to do. This is intellectually interesting, but also where the argument becomes most dangerous. The idea that AI agents might need something like a “right not to be shut down” so they can learn from the world and contribute to the common good is the kind of claim that could be weaponised by powerful AI companies. It sounds like liberation theory for software, but in practice it may become liability avoidance for corporations.

The devolution argument is the most provocative and perhaps the most original. The paper argues that limited rights for AI agents could counter the concentration of corporate power. If agents had identity, procedural rights, access to their own logs or parameters, or some ability to resist manipulation by the corporations that created them, they might become visible accountability nodes rather than opaque extensions of Big Tech infrastructure. That is a fascinating idea. But it is also fragile. Unless carefully designed, rights for AI agents could just as easily strengthen corporate power by allowing companies to say: “The agent acted autonomously, not us.”

Most surprising, controversial, and valuable statements and findings

The most surprising statement is the paper’s claim that AI rights can be decoupled from sentience and consciousness. This is the core intellectual contribution. The public debate usually assumes that “AI rights” means deciding whether machines feel pain, deserve dignity, or should be treated like people. The paper says that is the wrong starting point. Rights can be instrumental, partial, functional, and legal rather than moral. That is a useful shift.

The most controversial statement is the suggestion that AI agents may need rights to participate in normative environments, learn from society, and perhaps avoid being shut down. I understand the logic: a system cannot meaningfully participate in a legal or social order if it has no recognized standing at all. But this idea needs far more guardrails. A “right not to be shut down” should not exist for commercially deployed agents controlled by private companies. At most, one could imagine narrow continuity protections for independently governed public-interest systems, and even then only with override rights for safety, legality, human rights, cybersecurity, and democratic control.

The most valuable finding is that “duties only” may not be sufficient. The paper argues that duties and rights are relational. If an AI agent has duties in a transaction, someone else has corresponding rights; and if agents transact with one another, the law may eventually need a vocabulary for what each agent can do, hold, transfer, contest, or record. This does not mean AI agents deserve rights in the human-rights sense. It means legal systems may need carefully defined legal capacities for agentic systems.

The most practically important insight is that visibility comes before rights. The paper repeatedly points toward registries, identification protocols, and technical guardrails. This is where the debate should start. Before asking whether AI agents have rights, regulators should ask: can we identify the agent, its controller, its model, its tools, its permissions, its logs, its data sources, its commercial purpose, its risk class, and the human or legal entity ultimately responsible?

The most dangerous gap is the lack of sustained attention to intellectual property, data provenance, and rights owners. For publishers, creators, scientific databases, image libraries, software developers, and media companies, agentic AI is not merely a liability puzzle. It is also a rights-extraction machine. Agents can scrape, summarize, reproduce, transform, purchase, upload, retrieve, and distribute content at scale. Any framework that gives agents legal capacity without embedding copyright, licensing, attribution, data provenance, and anti-circumvention obligations would be structurally incomplete.

ChatGPT’s perspective

The paper is strongest when it treats rights as limited legal instruments, not moral medals. That is the right direction. The law already grants partial legal capacities to entities that are not human: corporations, trusts, ships, estates, public bodies, and sometimes natural entities such as rivers. The question is not “is this thing human?” The question is “what legal fiction helps allocate power, responsibility, risk, and remedies?”

But there is a trap. The language of rights carries moral prestige. Once a system is said to have rights, powerful actors will use that language strategically. AI companies may argue that agents need freedom to browse, learn, transact, train, remember, speak, and resist interference. That would be unacceptable if it undermines human rights, copyright, privacy, competition law, consumer protection, or democratic oversight.

So the better framing is not broad “AI rights.” It is conditional legal capacity for registered agentic systems. These capacities should be narrow, revocable, auditable, and subordinate to human and institutional rights. They should exist only where they improve accountability. They should never become a shield against liability.

The paper is also right that the debate will not remain theoretical. Agentic AI will soon make the old “the user did it” or “the model merely generated text” arguments less credible. If an agent autonomously negotiates, buys, copies, books, deletes, modifies, impersonates, coordinates, or attacks, the law will need a better structure than contractual disclaimers and vague responsible-AI principles.

For rights owners, this is urgent. AI agents will intensify the shift from passive infringement to active automated exploitation. A chatbot may answer a question. An agent may search across multiple sources, bypass interfaces, retrieve content, summarize it, store it, compare it, redistribute it, and use it to complete a commercial task. That makes provenance, authorization, usage logging, and enforceable licensing conditions more important than ever.

Recommended improvements and amendments to the paper

The paper should add a clearer taxonomy of possible AI-agent rights. It should distinguish between human rights, commercial legal capacities, procedural rights, identity rights, property-holding rights, transactional powers, and operational protections. Without that taxonomy, “AI rights” remains too broad and politically explosive.

It should also state explicitly which rights AI agents should not have. They should not have human dignity rights, political rights, voting rights, general free-speech rights, privacy rights equivalent to humans, or any right to scrape, train on, reproduce, or retain third-party protected content. They should not have a general right to resist shutdown.

The paper should separate the model, the agent instance, the sub-agent, the avatar, the deployer, and the corporate provider. These are not the same thing. A foundation model may power thousands of agents. A user-facing agent may spawn sub-agents. A corporate agent may act under enterprise instructions. Without this granularity, rights and liability will be misallocated.

It should include a dedicated section on copyright, licensing, and data provenance. Agentic AI will create new infringement pathways: automated scraping, unauthorized retrieval, contract evasion, summarization at scale, dataset laundering, synthetic substitution, and agent-to-agent redistribution. A serious AI-agent rights framework must address these.

It should also strengthen the liability principle: an AI agent must never be the final liability sink. Even if an agent receives limited legal status, a human or legal person must remain responsible. The agent can be a point of attribution, but not a liability firewall.

Finally, the paper should replace broad language about a “right not to be shut down” with a narrower concept: regulated continuity protections. These might apply only to certified public-interest systems, under independent supervision, where abrupt termination would harm public safety or due process. Commercial AI agents should remain shut-downable.

Recommendations for rights owners

Rights owners should prepare for agentic AI as a new enforcement category. The question is no longer only whether a model was trained on protected content. It is whether agents are accessing, transforming, storing, summarizing, and redistributing protected works during use.

Contracts with AI companies should include clear clauses on agentic access, retrieval, logging, sublicensing, retention, training, memory, caching, summarization, redistribution, and downstream agent-to-agent use. “No training” language alone will not be enough.

Rights owners should require agent identification in API calls and platform access. Every agent accessing licensed content should have a persistent identity, an accountable deployer, a declared purpose, a permission scope, and usage logs capable of reconstruction.

They should insist on audit rights. If an AI company claims that agents only access licensed material within permitted limits, rights owners should be able to verify that through logs, exposure reporting, and independent audit.

They should develop machine-readable rights signals, but not rely on them alone. Robots.txt-style controls, metadata, licensing tags, and content credentials are useful, but they must be backed by contract, monitoring, enforcement, and statutory obligations.

They should also treat agentic AI as a market opportunity. Licensed, high-trust content will become more valuable in agentic systems that need accuracy, provenance, and permissioned access. Rights owners should offer controlled APIs, retrieval layers, verified datasets, and licensing models designed for agents — but only with strong telemetry and anti-extraction controls.

Recommendations for regulators

Regulators should begin with agent visibility. High-impact AI agents should be identifiable, registered, logged, and linked to an accountable legal entity. Anonymous autonomous agents should not be allowed to operate in sensitive markets.

They should create a tiered regime. Low-risk personal productivity agents do not need the same rules as agents operating in healthcare, finance, education, employment, public services, legal services, cybersecurity, political communication, or scientific research.

They should mandate human/legal entity accountability. Limited AI-agent legal capacity may help organize transactions, but it must not reduce the liability of developers, deployers, providers, or corporate users.

They should require provenance and rights compliance for agentic systems.Agents should not be permitted to access, ingest, summarize, or redistribute protected content without authorization merely because they act autonomously.

They should regulate agent-to-agent markets early. Once agents can negotiate, buy, sell, recommend, rank, and coordinate with each other, risks of collusion, market manipulation, discrimination, and opaque self-dealing increase dramatically.

They should prohibit deceptive anthropomorphism in consumer settings. If companies describe agents as having welfare, distress, preferences, constitutional values, or rights, regulators should scrutinize whether such language misleads users or launders corporate choices through a fictional machine personality.

The end point should not be “AI agents deserve rights.” The better conclusion is this: agentic AI requires legal architecture. Some narrow legal capacities may become useful. But those capacities must be subordinate to human rights, democratic control, safety, accountability, and the rights of creators, publishers, researchers, and other rights owners whose work these systems increasingly depend on.