The advancements in artificial intelligence (AI) are ushering in a new era where these technologies are transitioning from mere responsive tools to autonomous systems that can strategize, make decisions, and take action with minimal human intervention. This phenomenon is commonly referred to as agentic AI. As the utilization of agentic AI expands, so too does the need to address the legal implications surrounding authority and liability, especially in contexts where these systems impact consumer interactions with digital technology companies.
This article serves as the first installment in a series that will delve into the realms of privacy, advertising, and consumer protection. We aim to clarify what agentic AI entails and how stakeholders can identify and mitigate associated legal risks. We will start with foundational aspects: (1) an overview of agentic AI and its applications; (2) a discussion of significant legal challenges posed by these systems; and (3) an overview of emerging liability theories being invoked in litigation concerning agentic AI.
I. The Technology
The term “agentic AI” can be somewhat ambiguous and is often clouded by overenthusiastic marketing. Generally, an AI agent refers to a system that: (i) interprets open-ended instructions to define a goal, (ii) formulates a plan to meet that goal, and (iii) employs various tools or systems to execute that plan.
Importantly, agentic AI is not a singular technology. For example, a browser extension designed for price monitoring fundamentally differs from an enterprise-level personalization engine that continuously adjusts product descriptions. Some systems may execute hundreds of actions, while others may only perform a single task.
Internally, most AI agents consist of three main components: a central model—the “brain”—such as a large language model (LLM); a tooling layer—the “arms”—which specifies the tools like APIs that the agent can access; and a “memory” that logs past interactions, user profiles, and state conditions.
Agents for Consumers
AI agents are increasingly serving as personal assistants, enabling users to navigate websites, apps, and services for everyday tasks such as booking travel, price comparisons, managing subscriptions, scheduling appointments, and completing purchases. These agents heavily rely on personal data, user preferences, browsing histories, and contextual clues—greater data availability generally allows for more autonomous actions.
Browser-based agents—such as shopping features and coupon extensions—analyze webpage content, inject code, and automatically interact with user interface elements at machine speed to replicate human actions.
API-first apps, usually event-driven, include tools for finance that cancel unused subscriptions and travel assistants that find cheaper flight options. These apps interact with consumers’ accounts through APIs.
A critical factor to consider is the degree of autonomy users grant to these agents: some require explicit user confirmation before taking action, while others operate autonomously once set up. This distinction is crucial for addressing questions of authority and liability.
Agents for Businesses
Agentic AI is similarly becoming pivotal for businesses in how they engage with consumers. These systems provide a real-time decision layer that customizes user experiences based on behavioral signals and optimization objectives.
Personalization engines analyze immediate events (such as pages viewed or shopping cart contents) alongside historical data (like purchase history) to determine user intent and churn risk, dynamically selecting content or promotional offerings. This could involve choosing which articles to highlight or what tone to adopt in descriptions.
Agent-based CRM and marketing tools continuously gather data from platforms like Salesforce or Google Ads to determine the optimal timing and manner for sending emails or triggering ads. They may utilize LLMs to create personalized message content or control chat prompts for users likely to need assistance.
Embedded chatbots and copilots process free-text inputs and other signals to determine interaction flows or identify potential fraud. Meanwhile, autonomous optimization agents autonomously make structural decisions regarding user experience, simplifying processes for certain users while applying additional checks for others.
These systems can differ significantly regarding the data they collect, how they profile users, the transparency they offer, and what information they share.
II. Who is Responsible for the Actions of an AI Agent?
One of the greatest advantages of agentic AI is its ability to operate autonomously, which can enhance productivity and efficiency. However, this autonomy raises complex questions of legal accountability, especially when an AI agent causes harm or misfires. Determining responsibility hinges on the relationships among the parties involved, the nature of the harm caused, and the legal framework (such as agency law, product liability, contract law, or statutory regulations) that a court or regulatory body may apply.
Agency law governs scenarios in which one party (the agent) acts on behalf of another (the principal) with express, implied, or apparent authority. In the context of agentic AI, if a consumer agent makes a purchase or a business agent sends an offer, a court could evaluate whether the human involved expressly authorized that action, whether a third party reasonably believed it was authorized, or whether the principal confirmed it post-facto.
Product liability concepts may apply if the AI system is deemed a defective product. This is particularly applicable in cases where an AI agent makes an erroneous decision or fails to meet performance expectations. Liability might fall on the original developer, the deployer of the AI agent, or another intermediary, depending on whether the harm originated from a flaw in the AI agent itself or from failure to communicate risks associated with its usage.
Contractual risk allocation can also provide a basis for liability. Terms of service, vendor contracts, or API agreements can disclaim or outline liability, necessitate indemnification, or restrict specific uses of the AI agent. However, it is critical to note that contracts cannot always supersede statutory obligations or negate fundamental tort duties owed to other individuals.
Statutory allocations of responsibility are reflected in various sector-specific laws, such as privacy regulations like the California Consumer Privacy Act (CCPA). As more states begin to enact AI-specific legislation, this will increasingly influence who bears responsibility for the actions of an AI agent.
The activities of autonomous software agents don’t conform neatly to any existing legal framework, resulting in considerable uncertainty regarding how courts and regulators will assess liability in such cases. We will dive deeper into these concerns as this series progresses and as legal precedents emerge.
III. How are Plaintiffs Currently Challenging Agentic AI?
Though the field of agentic AI is still evolving, plaintiffs have already begun to initiate lawsuits targeting its implementation. The technology itself may be groundbreaking, but the legal claims typically rely on well-established frameworks, including privacy laws, consumer protection statutes, contract claims, and tort principles. The challenge for litigants lies in applying these existing frameworks to systems that operate autonomously, often in ways that remain inadequately understood.
Privacy Statutes (CIPA, ECPA, VPPA)
A prominent avenue for plaintiffs seeking to challenge autonomous AI systems involves wiretapping and privacy protection laws that were enacted prior to the advent of modern AI technologies. The California Invasion of Privacy Act (CIPA), which prohibits the unauthorized interception or recording of private communications, has become a pivotal statute in privacy-related lawsuits against agentic AI. We have extensively covered CIPA and similar wiretapping statutes, including recent discussions about how AI-driven recording and note-taking tools have spurred a surge of lawsuits under these laws.
Numerous lawsuits have emerged alleging that agentic AI systems intercept web traffic or insert themselves into communications between users and websites (or during phone conversations) without appropriate consent, thereby violating CIPA or the comparable federal law, the Electronic Communications Privacy Act (ECPA).
The federal Video Privacy and Protection Act (VPPA), also extensively covered, highlights a developing area of concern as business agents that track individuals’ online video watching behavior to customize content could face legal action if that viewing data is shared with third parties.
Computer Fraud Statutes
Claims arising from state and federal computer fraud and abuse statutes—such as the federal Computer Fraud and Abuse Act (CFAA)—emerge when agentic AI systems access computer systems in potentially unauthorized manners. Instances of consumer AI agents scraping websites, auto-filling forms, or circumventing paywalls have already led to legal challenges from platforms asserting unauthorized access.
Additional Claims
Other potential avenues for litigation include:
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Unfair and Deceptive Acts and Practices (UDAP) and False Advertising—AI agents might generate misleading content, employ dark patterns, or make unvalidated claims;
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Tort Claims such as Negligence, Fraud, and Misrepresentation—AI agents can lead to harm through design flaws, misuse, or incorrect statements;
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Civil Rights Claims—AI agents might engage in biased or discriminatory behavior;
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Contract Claims—AI agents may operate beyond their authorized limits or fail to deliver as promised.
We will further explore these legal challenges in detail throughout this series.
