“AI Agents” are great. But what exactly are “agents”?

• Why treating these tools as just that – tools for humans – helps clear up worries about accountability, liability and “who’s to blame” when things go wrong.
• A plain-English explanation of what AI agents are (and what “agentic AI” means), how they differ from today’s general-purpose large-language-model tools, and some practical use-cases.
• Why using the term “agent” can be misleading – especially from a legal perspective – and what you should keep in mind when interacting with or deploying such tools.

Estimated reading time: 10 minutes

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1. What exactly are AI-agents (and agentic AI)?

Plain English definition

Let’s start with the basics. In the AI world, when people say “AI agent” they generally mean a piece of software that does more than just answer a single question. It can sense something (take input, ask questions, access data), reason/plan something (decide what to do), and act (use tools, trigger APIs, automate tasks) — all in pursuit of a goal the user has given it (or that it figures out). In a popular description:

“an AI agent is a system that perceives, decides, and acts in pursuit of a goal, often autonomously.”

The term “agentic AI” is a bit newer, and somewhat more ambitious: it refers to systems that go beyond being a single agent doing one task, to a multi-agent, orchestrated environment where different sub-tools or “agents” collaborate, decompose tasks, maintain memory, and adapt as they go. For instance, one agent might gather data, another plan, another execute a transaction, all coordinated to achieve a complex objective.

How this differs from current general-purpose LLM tools

Most of us today interact with large-language-models (LLM) tools (for example chatbots, writing assistants) that take a prompt and respond. They are useful, but their role is primarily reactive: you ask, they answer. They don’t generally persist memory across sessions (except through custom engineering), decompose multi‐step goals themselves, or proactively choose actions on your behalf.

By contrast, an AI agent (or an agentic‐AI system) might:

• Accept a goal such as “organize my business trip and invoice my  client” rather than “write me an email”.
• Break that goal into sub-tasks (book flight, reserve hotel, create invoice, update calendar).
• Use different tools/APIs (booking system, spreadsheet, e-mail) and coordinate across them.
• Keep track of state: which tasks done, what remains, what obstacles emerged.
• Possibly ask clarification questions, loop back, adapt when something changes (flight delay, hotel unavailable).

Thus it is more dynamic, multi-step, tool-enabled, and “goal-directed” than a simple prompt‐response model. Generative AI can hence be seen as a precursor, of AI Agents and Agentic AI systems as a paradigmatic shift marked by multi‐agent collaboration, dynamic task decomposition, persistent memory, and orchestrated autonomy (for more on this, look here).

Some practical use-cases

Here are some examples to ground this in real-world terms:

• Personal productivity assistant: You tell a system: “I need to plan a marketing workshop next month for 50 people in Berlin, within a budget of €10 K.” An AI agent might research venues, contact vendors, compare quotes, book the venue, send invites, monitor RSVPs, alert you if costs rise, etc.
• Customer-service workflow: In a business, instead of a human help-desk agent doing each step, an AI agent monitors incoming support tickets, triages them, triggers knowledge-base retrieval, escalates issues to humans when needed and follows up on changes automatically.
• Data-space / industrial twin scenario: Imagine a digital twin of a manufacturing asset (such as production machinery) in an industrial data space: you tell your AI agent to “monitor temperature, schedule maintenance if trend exceeds threshold, order replacement parts, print work-orders,” etc. The AI agent then carries out these tasks.
• Research/analysis orchestration: A team uses an “agentic” system that identifies research topics, collects relevant papers, summarizes them, flags gaps, proposes next experiments, and generates a draft report.

While many of these are still emerging (and not yet fully autonomous without human oversight), they illustrate the potential difference of AI agents from a mere “chatbot”.

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2. Why calling them “Agents” can be misleading

The legal meaning of “agent” and “agency”

When you use the term “agent” in everyday conversation, you might mean “helper”, “software assistant”, “bot”. Fine. But in law, “agent” (and “agency”) have very precise meanings. According to Black’s Law Dictionary, for example, “agency” is defined as:

“a relationship between two persons by agreement or otherwise where one (the agent) may act on behalf of the other (the principal) and bind the principal by words and actions.”  

But what exactly are “agents ? The dictionary further defines “agent” as

“someone who is authorized to act for or in  place for another”.

But aren´t there “electronic agents” in U.S. law?

Yes. Obviously, an AI system is not a “person” and does not have any legal capacity of its own. Interestingly though, in the United States, 15 USC § 7006 (3) introduced the term of an “electronic agent” and defines it as

“a computer program or an electronic or other automated means used independently to initiate an action or respond to electronic records or performances in whole or in part without review or action by an individual at the time of the action or response.”

However, this definition avoids the fallacy of granting such an “electronic agent” the status of a human agent who has its own legal capacity.

So what´s the catch?

In other words: legally, an agent is a human (or legal person) authorized by a principal, who acts on behalf of the principal and can create legal obligations (or rights) for the principal by that action. Under U.S. law, an AI agent is an electronic agent which is just a computer program. In civil law countries like Germany which have not adopted the concept of an “electronic agent”, the term “agent” only refers to a legal person (e.g. a limited liability company) or a human person, but not to an AI system.

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Why the question “But what exactly are agents?” matters for “AI agents”

So when we apply the term “AI agent” to a piece of software we introduce conceptual and legal confusion. Consider:

• The software is not a human person (or legal person) authorized by a human principal in the sense of the law.
• It cannot legally bind the principal by its own accord, unless the human user authorized and assumed the risk (and even then from a legal perspective the software is a tool, not an agent).
• If someone uses a system called an “agent” but expects it to have independent legal capacity, that expectation is mistaken.

Thus using the term “agent” may lead non-lawyers to believe the software has some independent authority or legal personality; it may lead to mis-assigning liability, mis-understanding who is bound by actions, or assuming accountability resides in the software rather than the human user. In short: you’re better off thinking of “AI agent” as a software tool with delegated automation rather than a legal agent.

A simple illustrative example

Imagine you have access to an industrial data space and you use a software-tool you refer to as an “AI agent” to place an offer for sale of a digital twin of a product. The software (your so-called agent) generates the offer and posts it in the data marketplace. Under the law, you (the human user) made the offer — because you used the tool to send the offer to the recipient, you selected the terms, you deployed it. The software itself cannot claim “I bound someone” or “I am the agent of the buyer/ seller”—it’s your tool. You are the principal, using a tool, hence you are bound by your own offer. If you mess up the terms, you’re liable. The term “agent” might mislead someone to think “the agent is binding itself, not me”, but legally that is wrong.

What you should keep in mind

• When you deploy “AI agents”, remember that the human user is the one exercising decision-making, and taking legal risk — the software is a tool.
• Language matters: if you say “the agent will sign the contract”, clarify that you mean you prompted the tool to execute the transaction and you remain responsible.

If you are making a declaration of intent (offer/acceptance) to a third party via the tool, you are the party to that contract, unless there’s explicit legal arrangement otherwise.

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3. Why treating “AI agents” as just tools helps with accountability

One of the frequent fears in media and some research is that AI may produce an “accountability gap” — i.e., if a software-agent makes a major mistake, who is to blame? Does the software have liability? Are humans off the hook? Is there impunity?

But if you start from the correct premise—i.e., the system is a tool under human direction rather than an independent agent—the picture becomes clearer:

• The human (or organization) using the tool retains responsibility for how it is set up, what instructions it receives, how outputs are used.
• If the tool causes damage or loss because it was mis-configured, mis-prompted or mis-used, the liability falls on the human or organization (or their insurer) unless specific laws provide otherwise.
• The notion of an accountability gap arises only if one mistakenly treats the software as an autonomous legal actor with legal capacity. But since that is not the case (under current law), there isn’t a true accountability gap — just human-tool combinations that must be governed properly.
• If there are situations where liability is limited by law (for example regulators decide not to hold operators liable in certain uses), that is a deliberate legal consequence rather than an inadvertent absence of accountability.

By recognizing the software as what it is (a tool) you retain clarity: humans make decisions, humans authorize actions, humans are accountable. That focus helps governance, auditing, risk management and trust.

Why this helps in practice

• Risk management: Organizations can assess “who gave the instructions to the tool?”, “who determined its parameters and who approved the output?”, “who will respond if something goes wrong?” If you erroneously treated the software as a “mini-agent” with its own legal capacity, you might lose that chain of responsibility.
• Audit trails and human oversight: If you keep humans accountable, then preserving logs, authorizations and human review become meaningful.
• Legal clarity: If a tool acts incorrectly and you are the user, you can’t shift liability to the software by pretending it was an “agent”. That mis-labelling may weaken your position in a dispute or regulatory inquiry.
• Ethical and transparent usage: When you say “we used an AI tool” rather than “we had an AI agent acting on our behalf”, you emphasize human accountability, reducing the risk of hype, mis-delegation or unrealistic expectations.

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Conclusion: So yes — AI agents are great. But don’t call them just “agents” – or at least, don’t forget what the word really means

The world of AI is evolving rapidly. The promise of “AI agents” and “agentic AI” to help us coordinate multi-step tasks, integrate tools, and free humans from tedious work is very real. We should welcome that innovation.

But we also should keep our feet on the ground. The term “agent” is tempting because it suggests autonomy, independent action, smart behaviour. But from a legal and conceptual standpoint it carries more weight than it actually should when applied to software. These systems are best thought of as tools under human direction—capable, advanced, but not independent legal persons or substitutes for human decision-makers.

When you deploy or discuss these systems, use precise language. If you label something an “AI agent”, clarify what you mean. Keep the human user or organization in the picture. Retain oversight, auditing and accountability. And if something goes wrong, recognize: you are the one who authorized the action or set the parameters. That clarity keeps your legal and risk posture healthy.

In short — embrace the innovation, but call it what it is: a software-tool with delegated automation, not a “legal agent”.

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