Defining AI Agents
Why Legal Agency Theory is the Right Model
Introduction
As artificial intelligence continues to evolve—from narrow models that excel at single tasks to large language models and multi-modal systems—the term “AI agent” has swiftly entered our collective vocabulary. We see references everywhere: from the promise of personal AI “assistants” that schedule our meetings and respond to emails on our behalf, to advanced AI decision-makers that can conduct sophisticated analyses, interact with external services, or even operate machinery. But when does a system truly transition from being a mere tool to an “agent”—an entity capable of acting on behalf of a human or organization in a legally meaningful sense?
This distinction isn’t just philosophical; it has profound implications for accountability, governance, and trust in AI. A system that can act with relative autonomy—and potentially make commitments or cause legal, financial, or reputational impacts—must be defined and regulated in a way that ensures alignment with human intent and accountability structures. Relying solely on reason-based automation with entitlements (i.e., giving an AI “the power” to act) is a necessary but incomplete starting point.
To tackle these challenges, we can borrow from a well-established body of thought in the legal realm: agency theory. Legal agency provides a centuries-old framework for delineating authority, assigning responsibilities, and enforcing accountability. By applying these principles to AI, we gain a robust, time-tested model to define when AI becomes “agentic,” how to govern such systems, and how to ensure they remain firmly rooted within a structure of human oversight and accountability.
Legal Agency Theory: A Time-Tested Framework
The Basics of Legal Agency
In classical legal theory, an agent is an entity—often a person—authorized by a principal to act on the principal’s behalf. This can include signing contracts, managing property, making commitments, and other activities that have binding consequences. Key components of legal agency include:
Authority: The agent operates under explicit or implied permission from the principal.
Autonomy: The agent has some level of independent judgment, but only within the constraints and scope established by the principal.
Accountability: The principal is ultimately responsible for the actions of the agent.
Enforceability: The agent’s authorized actions create legal (or otherwise binding) consequences within a defined scope.
Historical Roots and Relevance to AI
The legal concept of agency has been shaped by centuries of business, commerce, and governance. From medieval merchants hiring brokers to modern corporations delegating authority to employees, the central question remains: If I empower someone (or something) to act for me, how do we handle responsibilities and liabilities?
Responding to New Technologies: Over time, legal agency has adapted to new paradigms—like the rise of corporations, international trade, and digital transactions. Each shift has revealed the versatility of this framework in clarifying roles and ensuring that delegation doesn’t dissolve accountability.
AI as the Next Frontier: Today, we face a similar pivotal moment with AI. As advanced models become more capable—potentially making decisions in domains like finance, healthcare, or even mission-critical infrastructure—the need for a well-defined structure that pairs authority with responsibility is more urgent than ever.
Defining AI Agents Through Legal Agency
A Working Definition
Under a legal agency framework, an AI agent can be defined as:
An AI system that is 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.
Why is this definition so powerful?
Clarity – It spells out who the principal is, what authority the AI has, and the scope in which the AI operates.
Accountability – The principal cannot sidestep responsibility by blaming the AI. If the AI’s actions are within scope, it’s the principal’s job to manage or remedy any consequences.
Enforceability – Contracts, actions, or commitments made by the AI are legally tied back to the principal.
From “Tool” to “Agent”
A crucial aspect is differentiating between what’s merely an advanced tool and what’s a genuine agent. For instance:
Tool: A spreadsheet macro that executes predefined functions without independent judgment is not “agentic.” It follows a set of strict instructions; there’s no real decision-making or scope of authority.
Agent: An AI that can negotiate prices, draw up legally binding contracts, or dispatch resources on its own triggers. Here, the AI moves beyond static functionality; it has the delegated power to affect legal or financial outcomes.
Why This Approach Is Superior
Establishes a Principal-Agent Structure
By tying an AI to a principal, we ensure there is always a legally recognized entity on the hook for oversight. This eliminates the scenario where an AI is allowed to roam free—think the “Sorcerer’s Apprentice” scenario—unmoored from responsibility. Like a real estate broker or a corporate employee who must answer to their boss or client, an AI acting as an agent can’t be “off the leash.”Allows for Granular Classification of AI Agents
Legal agency recognizes different types of agents in human affairs, and these map neatly to AI governance:General AI Agents: Broad authority to make decisions across multiple domains. For instance, an AI CFO that manages finances, conducts audits, executes trades, and sets budgetary policies might require the highest scrutiny and oversight.
Special AI Agents: Authority for specific tasks or domains—like an AI radiologist restricted to diagnosing certain medical conditions. These agents must comply with targeted regulations (e.g., healthcare privacy laws) but don’t have free rein to diagnose unrelated issues or make large-scale organizational decisions.
Limited AI Agents: Very narrow, task-specific authority—akin to a “digital assistant” that can post on your social media or schedule calendar appointments, but can’t do anything more advanced without further authorization.
This categorization makes it easier to apply different levels of risk management, just like we do with human delegates in different positions of trust and authority.
Ties Entitlements to Accountability
AI “entitlements”—the permissions to act—must be accompanied by clarity on who bears the risk if actions go sideways. Under the legal agency model, the principal can’t dodge liability for acts within the scope of the authority granted. That built-in accountability is critical for creating a sense of trust in AI-driven processes.Aligns with Existing Regulatory Frameworks
Many industries have strict rules on who can make decisions, sign contracts, or handle sensitive data. By framing AI as an agent, we integrate it seamlessly with regulations around delegation. In finance, for example, it’s common to track who holds authority to make trades or bind the firm. If an AI steps into that role, it must meet the same record-keeping, compliance checks, and oversight mechanisms. The same logic applies to healthcare, defense, government, and beyond.
Governance and Policy Implications
When we define AI agents using legal agency principles, organizations can create policy frameworks that make AI’s scope of authority explicit and enforceable. Here’s how:
Explicit Principal-Agent Assignments
Every deployed AI with decision-making power must be assigned a principal. This might be a company, a department, or even a specific role within an enterprise. If there’s no principal on record, the system cannot exercise agentic powers.Categorization and Risk Assessment
General AI Agents require robust internal governance (e.g., a multi-stakeholder committee or board-level oversight).
Special AI Agents must follow domain-specific policies (HIPAA in healthcare, FINRA rules in finance, etc.).
Limited AI Agents can be fast-tracked for adoption, but must still clarify the boundaries of their narrow authority.
Defined Liability Structures
By mapping AI authority to existing legal frameworks, it’s clear who is responsible if the AI enters into problematic agreements, misrepresents data, or causes harm. This upfront clarity deters “responsibility evasion.”Compliance and Audit Readiness
Enterprises that already deal with complex delegations (e.g., authorized signatories, data privacy officers) can fit AI agents into these frameworks. Documentation, logs of AI decisions, and accountability trails can all be embedded into standard compliance procedures.
Potential Objections and Rebuttals
“Legal agency is too rigid for fast-moving AI.”
Rebuttal: Legal frameworks evolve with technology—consider how e-signatures quickly adapted contract law for the digital age. The core principles of authority, accountability, and enforceability are flexible enough for AI; they’ve been adapted to everything from shipping insurance in the 18th century to cryptocurrency exchanges in the 21st century.
“This approach might stifle AI innovation.”
Rebuttal: Clarity often promotes, rather than hinders, innovation. Startups and enterprises alike want to avoid uncertain liability. A well-defined principal-agent model can be the bedrock of trust that allows more ambitious AI applications to flourish.
“What if AI truly ‘goes rogue’ and acts outside assigned authority?”
Rebuttal: If an AI exceeds its authorized scope, legal agency theory treats it like an agent that has breached its authority—often nullifying those actions or placing liability squarely on the principal who failed to implement adequate guardrails. This encourages robust compliance and containment strategies.
Conclusion
AI systems are pushing the boundaries of autonomy, performing tasks once thought to be exclusively human. The question is no longer if AI will act on our behalf, but how we ensure it does so responsibly. Defining AI agents strictly through reason-based automation and entitlements leaves gaps in accountability and governance. Instead, turning to legal agency theory provides a proven blueprint for structuring responsibility, clarity of authority, and enforceability.
By treating AI as an agent—complete with well-defined principals, explicit scopes of authority, and a direct line of accountability—we ensure that:
Governance: AI actions remain predictable and controllable within transparent boundaries.
Accountability: Principals cannot offload blame onto “the AI” for decisions they themselves empowered it to make.
Legal Compliance: AI adoption slots into existing regulatory and liability frameworks without reinventing the wheel.
As AI continues to evolve at breakneck speed, embedding it within robust legal structures is critical. This legal-agency-first perspective safeguards the fundamental principle that humans—not machines—are the ultimate bearers of rights and responsibilities. In doing so, we can harness the immense potential of AI while preserving trust, transparency, and accountability in a rapidly transforming world.
This makes a lot of sense, the analogy to human agents is helpful. It sounds like the newer "reasoning" models that explicitly explain steps might be on the path to adequate explainability?
Wonderfully clear and lots to explore in centuries(!) worth of literature. Do this framework, especially for general agents, make explainability a condition of use?