Bridging the AI Agent Definition Gap with Legal Agency Theory

Bridging the AI Agent Definition Gap with Legal Agency Theory

Silicon Valley may be abuzz about “AI agents,” but as a recent TechCrunch piece laments, no one can seem to agree on what an AI agent is, exactly (No one knows what the hell an AI agent is | TechCrunch). The term “agent” is being applied to everything from simple chatbots to autonomous decision-making systems, to the point of becoming diluted and almost meaningless (No one knows what the hell an AI agent is | TechCrunch). This ambiguity isn’t just semantic nitpicking—it has real consequences. Without clarity, organizations struggle with misaligned expectations and cannot easily align AI capabilities with accountability or measure outcomes (No one knows what the hell an AI agent is | TechCrunch). Is an “AI agent” just a fancy autocomplete, or is it something with greater autonomy and responsibility?

In this post, I argue that we can define what the hell an AI agent is – by borrowing a page from centuries-old legal agency theory. By treating AI systems as analogous to legal agents, we gain a clear framework to define and govern them. This approach can cut through the hype and provide the much-needed clarity and accountability that the TechCrunch article found lacking.

The Buzzword Chaos: Why “AI Agent” Has Become Ambiguous

The TechCrunch article cataloged a veritable identity crisis around the term “AI agent.” Different companies use it in vastly different ways, adding to widespread confusion (No one knows what the hell an AI agent is | TechCrunch):

• OpenAI: In one week, OpenAI described agents in two conflicting ways – first as “automated systems that independently accomplish tasks on behalf of users,” and then as “LLMs equipped with instructions and tools” (No one knows what the hell an AI agent is | TechCrunch). Even OpenAI’s own team used agent interchangeably with assistant, muddying the waters further (No one knows what the hell an AI agent is | TechCrunch).

• Microsoft: Microsoft’s blogs draw a fuzzy line between AI assistants and agents. Agents are hyped as “new apps” with particular expertise, whereas assistants handle generic tasks like email drafting (No one knows what the hell an AI agent is | TechCrunch).

• Anthropic: Anthropic admitted the term can mean almost anything, from “fully autonomous systems that operate independently over extended periods” to simple “prescriptive implementations that follow predefined workflows” (No one knows what the hell an AI agent is | TechCrunch). In other words, agent could describe a long-running autonomous bot or just a scripted routine.

• Salesforce: Salesforce went broadest of all, essentially calling any system that can respond without human intervention an agent (No one knows what the hell an AI agent is | TechCrunch). Their taxonomy spans six categories from “simple reflex agents” to “utility-based agents,” lumping trivial triggers and complex AI under one umbrella.

With everyone marketing their own flavor of “agent,” it’s no surprise the term has become nebulous. As TechCrunch noted, “agent” and “agentic” are now so overused that they’re bordering on meaningless (No one knows what the hell an AI agent is | TechCrunch). AI luminary Andrew Ng observed that what used to be a technical term got hijacked by marketers, diluting any precise meaning (No one knows what the hell an AI agent is | TechCrunch). This lack of a shared definition is more than a linguistic quibble – it leads to confusion for customers and developers and makes it “challenging to benchmark performance and ensure consistent outcomes,” as one AI executive warned (No one knows what the hell an AI agent is | TechCrunch). In short, the current ambiguity around AI agents threatens both trust and tangible progress.

A Principled Solution: Enter Legal Agency Theory

How do we resolve this conceptual chaos? Legal agency theory offers a powerful, clarifying lens. Taylor Black, in his February 2025 Substack article “Defining AI Agents: Why Legal Agency Theory is the Right Model,” argues that we should define AI agents by analogy to legal agents (Defining AI Agents - by Taylor T Black - Poured Brews). In law, an agent is not a buzzword; it’s a well-defined role with specific criteria. Applying those time-tested principles to AI yields a concrete model for when an AI system truly becomes an “agent” in a meaningful (and governable) sense (Defining AI Agents - by Taylor T Black - Poured Brews).

So what does legal agency entail? In classical terms, an agent is someone authorized to act on behalf of another (the principal) in a way that can create binding obligations (Defining AI Agents - by Taylor T Black - Poured Brews) (Defining AI Agents - by Taylor T Black - Poured Brews). Key components of legal agency include authority, autonomy (within a scope), accountability, and enforceable outcomes (Defining AI Agents - by Taylor T Black - Poured Brews):

• Authority: The agent has explicit or implied permission from a principal to act in certain matters (Defining AI Agents - by Taylor T Black - Poured Brews). In an AI context, this means a human or organization delegates power to the AI for specific tasks. An AI without granted authority is just a tool operating at a user’s direct command, not an independent agent.

• Autonomy (Within Scope): The agent can exercise some independent judgment, but only within the limits set by the principal (Defining AI Agents - by Taylor T Black - Poured Brews). Similarly, an AI agent might make its own decisions on how to achieve a goal, but what it’s allowed to do is bounded by its mandate. This notion distinguishes a truly agentic AI from a mere program following a script.

• Accountability: Critically, the principal remains ultimately responsible for the agent’s actions (Defining AI Agents - by Taylor T Black - Poured Brews). If an AI agent does something on your behalf, you (or your company) are on the hook for the outcome, just as if a human agent acted for you. The AI cannot be a convenient scapegoat to dodge liability – the buck still stops with the human who deployed it.

• Enforceable Outcomes: The agent’s actions carry weight and consequences that are legally or contractually binding for the principal (Defining AI Agents - by Taylor T Black - Poured Brews). In other words, an AI agent’s decisions or commitments actually count in the real world – whether it’s spending money, making agreements, or causing impacts that someone could be held liable for. If an AI’s “actions” have no direct consequences (say it just suggests options), then that AI is functioning more like an advisor or tool, not an agent.

By using this framework, we can craft a working definition for an AI agent that cuts through vagueness. Black proposes the following definition, which sums it up well:

An AI agent is an AI system explicitly authorized to act on behalf of a principal, with the ability to make reason-based decisions in line with the principal’s objectives, and whose actions create enforceable consequences within a defined domain (Defining AI Agents - by Taylor T Black - Poured Brews).

This definition is powerful because it specifies who is in charge (the principal), what the AI is allowed to do (its scope of authority), and the fact that the AI’s actions have real stakes (they’re enforceable and tie back to the principal). It bakes in accountability by design. As Taylor T. Black emphasizes, this approach brings much-needed clarity: it ensures everyone knows the boundaries of the AI’s role, prevents principals from shirking responsibility, and ties any commitments made by the AI directly back to its human overseer (Defining AI Agents - by Taylor T Black - Poured Brews).

Tool or Agent? Distinguishing Software from Software Agent

Crucial in this legal perspective is the bright line it draws between a tool and an agent. Not every advanced piece of software should count as an AI agent, even if it’s smart or autonomous in some ways. It comes down to the level of independent authority and the weight of its actions.

Consider a simple example from Black’s article: a spreadsheet macro can perform complex operations automatically, but it’s not exercising judgment or making independent commitments – it just follows predefined instructions. That’s a tool, not an agent (Defining AI Agents - by Taylor T Black - Poured Brews). No one would expect a macro to sign a contract or decide to transfer funds on its own, and if it miscalculates, it’s treated as a software error (the user is responsible for using the tool correctly).

Now consider an AI system empowered to, say, negotiate prices with vendors for you, or to draft and sign off on routine contracts within set parameters. That system is making decisions with some discretion (within limits you set) and could enter you into binding agreements. That looks a lot more like an agent – the AI has been given delegated power to affect legal or financial outcomes on your behalf (Defining AI Agents - by Taylor T Black - Poured Brews). If it commits to a purchase or signs a deal, you’re committed to it as if you did it yourself.

The difference hinges on those legal agency concepts: the AI agent has authority from you, operates autonomously but within your defined scope, and creates obligations that you (as principal) must answer for. A mere tool might be clever or automated but does not have that independent agency. Using these criteria, we can avoid labeling every flashy AI demo an “agent” and reserve the term for systems that truly act for someone in a meaningful capacity.

This distinction is not just academic. It has real implications for how we govern AI. If we mislabel tools as agents, or vice versa, we either impose too much overhead on simple tech or, conversely, fail to put necessary safeguards around powerful autonomous systems. Legal agency theory gives us a test for agentic AI: Does it have delegated authority with real accountability? If yes, treat it like an agent; if not, it’s just a tool or an assistant. This clears up the confusion by providing a standard filter.

Aligning AI Power with Accountability and Enforceability

One of the biggest advantages of a legal-agency definition is how it aligns an AI system’s capabilities with accountability. In the current free-for-all usage of “agent,” it’s often unclear who is responsible if an AI agent misbehaves or causes harm. Companies might tout “autonomous agents” and then shrug when things go wrong: after all, if no one knows what an agent truly is, it’s easy to evade responsibility. The legal model closes that loophole. By definition, an AI agent must have a principal who bears responsibility for its actions (Defining AI Agents - by Taylor T Black - Poured Brews). In practice, that means whenever we deploy an AI agent, we also establish who will answer if it makes a bad call.

This built-in accountability has profound importance for trust and governance. It ensures that any AI powerful enough to act on our behalf is never operating in a vacuum. There is always a human or organization on the hook, much like a company is responsible for its employees or a client is for their attorney. In Taylor Black’s words, tying AI to a principal “eliminates the scenario where an AI is allowed to roam free — think the ‘Sorcerer’s Apprentice’ scenario — unmoored from responsibility.” (Defining AI Agents - by Taylor T Black - Poured Brews) In other words, an AI agent can’t be let “off the leash” with no one accountable for what it does.

Equally important is the idea of enforceable outcomes. Under the legal approach, if your AI agent does something within its authorized scope, you are legally treated as having done it. This means any promises it makes, any transactions it executes, any content it publishes, are binding on you (assuming you gave it that authority). Conversely, if an AI somehow goes rogue and acts outside the authority you gave it, those actions should not bind you — just as in law, a principal isn’t bound when their agent exceeds their mandate. Instead, it’s a signal of system failure or oversight failure on the principal’s part, which carries its own consequences. This concept aligns AI autonomy with enforceability: an AI can only commit its principal to the extent the principal empowered it to do so (Defining AI Agents - by Taylor T Black - Poured Brews). Any further, and the fault lies in poor controls rather than a magical “AI made me do it” excuse.

By framing things this way, we directly address the TechCrunch article’s concern that everyone’s building agents with “misaligned expectations”. If organizations adopt a legal agency perspective, they must clarify expectations upfront – what the AI is allowed to do, what it’s accountable for, and who answers for it. That makes expectations explicit and measurable. Jim Rowan’s worry about difficulties in measuring value or ROI for agentic projects (No one knows what the hell an AI agent is | TechCrunch) also gets relief: if you know the exact role and scope of your AI agent, you can benchmark its performance against that defined role. You’re no longer comparing apples to oranges (or chatbots to autonomous robots) under the vague label “agent” – you have categories like Limited Agent, Special Agent, General Agent (akin to Black’s tiers of narrow vs. broad authority agents) and can evaluate each on appropriate metrics (Defining AI Agents - by Taylor T Black - Poured Brews) (Defining AI Agents - by Taylor T Black - Poured Brews).

From Confusion to Clarity: Why Legal Agency Model Solves the Dilemma

The beauty of using legal agency theory is that it doesn’t squelch the innovation around AI agents – it structures it. In fact, clarity in definitions can promote innovation rather than hinder it (Defining AI Agents - by Taylor T Black - Poured Brews). When developers and users know the rules of the road, they can push the boundaries in a responsible way. Imagine an AI startup being able to say: “Our product is an AI agent under a well-defined principal-agent contract: here’s what it’s allowed to do, here’s how we audit its decisions, and here’s who takes responsibility.” That is far more reassuring (to customers, regulators, investors) than a nebulous promise of an “autonomous agent” that might do anything. As Black notes, a clear principal–agent model can be the bedrock of trust that allows more ambitious AI applications to flourish safely (Defining AI Agents - by Taylor T Black - Poured Brews). Companies can innovate on capabilities while staying within guardrails that make accountability enforceable.

By adopting this model, much of the current conceptual confusion evaporates. We no longer have to twist ourselves in knots over whether a particular AI is an “agent” or just an “assistant” – we can ask plain factual questions: Has the AI been delegated authority to act on behalf of someone? Is it operating within a defined scope? Are its outputs carrying real-world consequences that someone is accountable for? If yes, it’s an agent by definition. If not, call it a tool or assistant, but don’t imbue it with the aura of agency.

This approach directly addresses the ambiguity highlighted by TechCrunch. The article pointed out that an Amazon “agent” isn’t the same as a Google “agent,” leading to customer confusion (No one knows what the hell an AI agent is | TechCrunch). Under a legal-agency view, Amazon and Google could actually describe their AI products in common terms of authority and responsibility. For instance, Amazon’s shopping AI could be described as a Limited AI Agent authorized to make purchases up to a certain dollar amount on a user’s behalf (with the user ultimately accountable for those purchases), whereas Google’s Project Mariner might be a General AI Agent with broader decision powers but still reporting to a designated principal within an enterprise. These aren’t just marketing labels; they tell you exactly what the AI can do and who is answerable for it.

Furthermore, a legal framework for AI agents dovetails with existing laws and regulations, rather than inventing something completely new. We already have compliance mechanisms for human agents in finance, healthcare, etc. – extending them to AI agents is a logical step (Defining AI Agents - by Taylor T Black - Poured Brews). If an AI is effectively acting like your employee or representative, then it should follow the same rules your human representative would. This means the “agent or assistant?” debate can be sidestepped: we only call it an agent (in any serious sense) when it’s taking on a role that a person could have taken on with legal accountability. Everything else is just software support. That greatly simplifies governance. As Black writes, “turning to legal agency theory provides a proven blueprint for structuring responsibility, clarity of authority, and enforceability” in AI deployments (Defining AI Agents - by Taylor T Black - Poured Brews). Rather than each company inventing an “agent” concept to suit its marketing, we anchor the concept in a stable legal bedrock that has served us for ages in human contexts.

Conclusion: Clarity, Accountability, and a Call to Action

The term “AI agent” doesn’t have to remain a riddle or a marketing buzzword. By embracing legal agency theory as a defining framework, we can reclaim and refine the meaning of AI agents. This shift brings a number of concrete benefits: clarity about what an AI agent is and isn’t, accountability by ensuring every agent has a responsible principal, and enforceability of the agent’s actions within established legal bounds (Defining AI Agents - by Taylor T Black - Poured Brews). It transforms the notion of agent from a vague “maybe it’s autonomous?” hype into a specific contract of authority and responsibility.

For the technical community and general audience alike, the takeaway is that we already have the tools to define and govern AI agents – we just need to apply them. The centuries-old principles that govern human agents (like power of attorney, fiduciaries, corporate agents) can guide us in taming AI agents. This means when you hear a company boasting about its new AI agent, you should ask: Who’s the principal, what’s the scope of its authority, and who is accountable for its actions? If they can answer clearly, great – it’s a sign they’re treating the AI with the seriousness of an agent. If not, maybe they’re just dressing up a fancy tool with a trendy name, or worse, deploying a powerful AI without proper accountability.

It’s time for industry leaders, developers, and policymakers to coalesce around a more rigorous definition of AI agents. We don’t need to wait for some speculative “AGI” to figure out how to handle AI that acts on our behalf; the legal paradigm already gives us a strong foundation. By making AI accountability commensurate with AI capability, we ensure that as our systems grow more autonomous, we don’t sacrifice control, trust, or safety. In a fast-evolving AI landscape, clarity is our friend. Adopting a legal agency model for AI agents is a decisive step toward demystifying this buzzword and building a future where AI systems can be innovative and useful without becoming ungovernable or irresponsible.

Call to Action: Whether you’re a developer building the next “agentic” app or a business leader integrating AI into your operations, start using the language of principals and agents. Define the roles, set the scopes, and assign the accountability. By doing so, we as a community can move beyond the rhetorical question of “what is an AI agent?” to actually reaping the benefits of AI agents that are well-defined, well-governed, and worthy of our trust.