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AI agents are rapidly gaining the ability to do more than answer questions. They can browse websites, execute code, interact with business software, make purchases and complete complex tasks with increasingly limited human supervision.

But giving software the ability to act independently creates an uncomfortable question:when an AI agent causes harm, who is responsible?

The answer is becoming harder as agents grow more autonomous.

An AI agent is not a legal person. It cannot sign a contract, pay damages or be held accountable in the same way as an individual or corporation. When something goes wrong, responsibility must ultimately return to the humans and organizations behind the system.

The difficult part is deciding which ones.

A single agent can involve several parties: the company that developed the foundation model, the developer who built the application, the organization that deployed it, third-party services connected through APIs or plugins, and the user who gave the original instruction.

The agent itself may then decide how to accomplish that instruction.

That makes responsibility much less obvious than with traditional software.

A user might ask an agent to research a competitor, organize company files or negotiate with a supplier without specifying every intermediate action. If the agent accesses information it should not, exposes confidential data or enters into an unwanted transaction, the harmful action may never have been explicitly requested.

Yet someone still gave the system the authority to perform it.

This distinction between intent and delegated authority could become central to future disputes involving autonomous AI.

Prompt injection demonstrates how complicated the situation can become.

Imagine an employee asks an AI agent to review incoming emails. One message contains hidden instructions designed to manipulate the agent into retrieving confidential information and sending it somewhere else.

Who is responsible if the attack succeeds?

The attacker deliberately manipulated the system, but the agent had access to the information. The employer granted those permissions. The application developer designed the workflow, while the model provider created the underlying AI.

There may not be a single obvious point of failure.

That is why AI security and AI liability are beginning to converge. Permissions, sandboxing, monitoring and human approval are no longer merely technical decisions. They may also help determine whether an organization took reasonable precautions before allowing an autonomous system to act.

The simplest way to reduce uncertainty is to limit what agents can do without approval.

An AI system drafting an email for a human to review creates a clear decision point. An agent independently sending messages, transferring money or modifying production infrastructure removes it.

That does not mean every agent action needs human supervision. It means autonomy should probably reflect the potential consequences.

Routine tasks can happen automatically. Financial transactions, data deletion, security changes or external communications may deserve stronger controls.

The question for organizations becomes less about whether an agent can perform an action and more about whether they are prepared to accept responsibility when it does.

When incidents occur, organizations will also need to reconstruct what happened.

That requires knowing who initiated the task, which model was involved, what information the agent received, which tools it called, what permissions it exercised and whether other agents participated.

In effect, autonomous systems may require a chain of custody for machine actions.

Without detailed audit trails, companies may struggle to explain why an agent made a particular decision — not only to security teams, but potentially to customers, insurers, regulators and courts.

This makes observability an increasingly important part of AI governance.

The liability debate reveals something fundamental about agentic AI: delegating a decision to software does not make responsibility disappear.

It simply moves responsibility somewhere else.

Companies deploying autonomous agents will increasingly need clear identities, limited permissions, audit trails, spending controls, sandboxing and approval requirements around sensitive actions.

These mechanisms will not prevent every mistake. But they can limit the consequences and establish who authorized what.

As agents become capable of acting with less human involvement, the most important question may no longer be how autonomous they can become.

It may be how much autonomy organizations are willing to be accountable for.