The marketplace for AI-powered personal agents is rapidly evolving. Companies like Amazon and Salesforce are already offering services that help consumers navigate online shopping, manage subscriptions, and automate routine tasks. These developments signal a shift in how we interact with digital services and make purchasing decisions.
Consumer Reports is exploring the potential for developing pro-consumer AI agents that prioritize user interests above all else. This approach comes with unique legal and design challenges that set it apart from purely commercial offerings.
The idea of using such agents has the potential to fundamentally reshape how consumers use their data, navigate complex services, and make decisions. If developed thoughtfully, these agents could safeguard privacy and act as trusted intermediaries. While there are many interesting questions of law and practice to enable and safeguard this, thankfully, existing legal frameworks have already begun to anticipate and support such innovations.
In this post, I’ll examine three key areas:
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- The existing legal framework that supports the use of AI agents for transactions;
- Design paths for creating truly user-centric AI agents; and
- The potential impact of these agents on consumer empowerment in the digital marketplace.
By understanding these foundations, we can work towards AI agents that genuinely serve consumers’ best interests.
The Forgotten Framework: UETA and the Rise of LLM Agents
For decades, the law has envisioned a world where electronic agents can represent us and act on our behalf. In 1999, the Uniform Electronic Transactions Act (UETA) laid out a framework for e-commerce. UETA was created to address the legal uncertainties surrounding electronic transactions and to provide a consistent framework across states.
This law is the very reason we can confidently use electronic signatures and contracts in our daily digital interactions. It is a cornerstone of the information age, providing legal certainty for online commerce and other electronic transactions. More to the point, UETA provides explicit provisions for electronic agents to conduct transactions autonomously.
This uniform law has been adopted across the United States, statutorily enacted in 52 states and territories, and truly is the law of the land. This legal foundation provides clear definitions and applicable rules for concepts like electronic signatures, automated transactions, and attribution for the acts of autonomous agents. Fast forward to today, and this vision can finally come to life impactfully by supporting the use of new software services, including advanced AI assistants and LLM-based agentic software applications, including for individuals.
This existing legal foundation provides clear definitions and rules for key concepts:
- Electronic Agent: “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.” This definition perfectly describes the capabilities of LLM-powered AI agents.
- Automated Transaction: “A transaction conducted or performed, in whole or in part, by electronic means or electronic records, in which the acts or records of one or both parties are not reviewed by an individual in the ordinary course in forming a contract, performing under an existing contract, or fulfilling an obligation required by the transaction.” This clarifies the legal validity of agent-led transactions.
- Attribution: UETA also establishes how to determine on whose behalf an electronic agent is operating, ensuring accountability. Essentially, under UETA, an electronic record or signature is attributable to a person if it was the act of that person, which can be shown in any manner, including the efficacy of any security procedures applied.
LLM agents may make unexpected errors when conducting automated transactions, which is a significant concern. UETA establishes a framework for error prevention and correction, particularly emphasizing agreed-upon security procedures. For instance, a consumer and an online retailer could establish a spending limit for the consumer’s AI agent. If the agent attempts to exceed this limit, the security procedure would trigger an alert, preventing the error.
Importantly, if a merchant fails to implement an agreed-upon security procedure and an error occurs, UETA provides the consumer with the right to reverse the transaction. Conversely, if the retailer fails to implement an agreed-upon security procedure, such as verifying the purchase amount with the consumer before finalizing the transaction, and the agent makes a purchase beyond the agreed-upon limit, UETA could provide the consumer with legal grounds to reverse the transaction and recoup the excess funds.
With the emergence of AI agents, we now have the technology capable of meaningfully fulfilling UETA’s vision. These agents can communicate in natural language, negotiate, retrieve information, and even execute decisions—but critically, they can also be built to operate on behalf of the consumer, avoiding conflicting interests. Rather than invent new legal frameworks, we can leverage and extend existing ones like UETA to achieve predictable legal outcomes and accelerate the responsible development of personal AI.
For example, imagine a personal AI agent negotiating a better price for a subscription service on your behalf. Under UETA, this automated transaction would be legally binding, just as if you had negotiated it yourself.
Beyond price negotiation, such agents could automatically handle your insurance claims, gather quotes for home repairs, or even help you manage your investments according to your risk tolerance. Imagine receiving proactive alerts from your AI agent about better deals on services you frequently use or having it automatically adjust your utility plans based on your actual consumption patterns to save you money. These examples illustrate the potential of personal AI agents to simplify our lives and give us more control over our interactions with complex systems.
Such capabilities make LLM agents uniquely suited to leverage the legal framework established by UETA and extend it to new domains of personal empowerment.
Being Loyal: Building Agents That Work for You
While UETA provides the legal foundation for AI agents, the next step is to ensure these agents can operate securely, reliably, and in alignment with the user’s interests. The most compelling use case for personal AI agents is their ability to advocate on behalf of consumers without bias or conflicting interests. Unlike AI systems embedded within purely profit-seeking enterprises, or to advance a commercial objective or to fulfill a narrow “customer service” framework, personal AI agents could be entrusted with a “duty of loyalty” that binds the service provider to operate the agent in the best interests of the user. These agents could manage tasks like travel bookings or e-commerce purchases with the same trustworthiness as a high-end fiduciary representative, advocating only your interests.
Robust encryption, privacy standards, and transparent data stewardship practices could bolster this trustworthiness. These types of measures begin with terms of service and governance-based assurance, and that are also be encoded into the design of the system. To achieve this level of trust, AI agents must also implement clear attribution mechanisms. This means that any action the agent takes can be reliably traced back to the user, establishing accountability and legal responsibility.
Looking ahead, it’s crucial to consider how AI agents will interact not just with traditional online systems, but where AI agents negotiate and transact with each other on our behalf. This could lead to a more efficient and potentially fairer marketplace. For example, your personal AI agent could automatically negotiate the best price for a product or service by interacting with the AI agents of multiple vendors, comparing offers, and securing the most favorable terms, all while adhering to your pre-defined preferences and limits. Furthermore, exploring concepts like delegation of authority in multi-agent systems can pave the way for even more powerful consumer empowerment tools. While LLM agents can interact with natural language and web-based systems surprisingly well, eventual high-velocity agent-to-agent transactions would require the development of common protocols and standards for inter-agent communication and negotiation. However such standards are not needed to use LLM agents with existing online services and platforms. UETA envisions and supports transactions with one electronic agent, two electronic agents, or large numbers of electronic agents. There is room to grow under the existing law.
With this robust legal foundation in place, the next challenge is to design AI agents that not only comply with these laws but also operate effectively on behalf of consumers.
Design Paths for Consumer AI Agents
Designing consumer AI agents requires a thoughtful approach to balancing security, user experience, and legal or regulatory considerations. Consider the following three potential models, each with its own advantages and challenges:
- Full Authentication Model whereby the agent uses the same authentication and authorization credentials as its user;
- Intermediary Model, whereby the agent is operated by another party who uses the agent to act on the user’s behalf; and the
- Decentralized Identity Model, whereby the agent leverages decentralized identifiers and verifiable credentials to interact with third parties, giving users direct control over their digital identity.
Each model presents unique trade-offs and aligns differently with user trust, system complexity, and risk frameworks. Let’s examine each of these design paths in more detail, considering their strengths, weaknesses, and potential applications in the context of consumer AI agents.
Full Authentication
The Full Authentication path positions the AI agent as a direct extension of the user. By acting with the user’s authorization and utilizing their credentials and permissions, the agent can access online platforms, add items to shopping carts, compare prices, and even complete purchases autonomously according to pre-defined rules. The main strength of this approach is its simplicity. It uses current technology to enable the AI agent to perform various tasks without requiring companies to develop new infrastructure or protocols. This seamless interaction is achieved through existing standards, making it easy to deploy and integrate.
To effectively execute this, the agent would need capabilities to interact using the same interfaces that would be made available to an authenticated user.
However, this approach also carries significant security risks, as it requires granting the agent extensive access to user accounts and credentials. There are also substantial risks in terms of data stewardship and liability (and it’s for this reason regulators have discouraged the use of screen scraping and are encouraging the development of more secure interfaces for third-parties to authenticate on a users’ behalf).
For instance, if the agent misuses the data or performs unintended actions, it may be unclear who should be held responsible—the user, the agent provider, or the third party with whom the agent transacted. Moreover, compliance with data protection and privacy regulations like GDPR or CCPA is more challenging to implement because the agent’s full access could potentially implicate user data rights. This ambiguity can hinder adoption, as users and companies may be reluctant to grant the required level of permissions.
Intermediary Path
In contrast, the Intermediary path positions the AI agent as part of a distinct entity that acts as a negotiator or advocate on behalf of the user. Instead of using the user’s credentials, the agent operates under its own identity and permissions, creating a clear separation between the user and the agent service provider. Here, the agent is provided to the user by another party, such as a consumer group or other service provider, and is designed to operate on behalf of the user with third parties like online vendors.
In this setup, the agent operates under a set of rules that define its role, allowing it to handle transactions, share specific data points, and communicate the user’s preferences in a controlled manner. This granular control may empower users with greater agency over their data and privacy. To enable this, the Intermediary path would require new protocols or handshake mechanisms to establish the agent’s legitimacy and scope of authority with third parties like online merchants and other organizations the user seeks to transact with.
To function effectively as an intermediary, the agent could leverage existing standards like OAuth 2 and OpenID Connect, but in a different way. In effect, the intermediary acts as an authorized application of the consumer, with permissions explicitly granted by the user to take specific actions on their behalf. This means the intermediary holds tokens or authorizations that permit it to execute tasks as the user’s representative without the agent ever directly holding the user’s core credentials. This model maintains a clear distinction, allowing the intermediary to act independently while still adhering to permissions that have been transparently defined and authorized by the user.
This approach offers a range of advantages, primarily stemming from the clear delineation of roles and responsibilities, which helps simplify accountability and can foster greater trust. In a technical legal sense, the party providing the agent service would be the legal “agent” of the user in this case, greatly clarifying the roles and relationships with the user (who would be the “principal,” legally) and third parties with whom transactions are conducted. In simpler terms, this means the organization providing the AI agent service could be legally responsible for the agent’s actions, because that organization is the legal agent and may owe the user a duty of care to act reasonably and competently.
This separation clarifies liability and simplifies compliance with data protection and other laws. Furthermore, the agent can engage in advanced activities such as dynamic pricing negotiations or crafting customized agreements with service providers, offering enhanced value to the user. However, a significant challenge in adopting the Intermediary path lies in the need for standardization. Creating the necessary infrastructure and achieving industry-wide consensus on configuring existing protocols in new ways (eg, to support an authorized agent role with standards like OAuth 2 and OpenID Connect) and filling the remaining gap with new protocols involves substantial coordination and time, making this a more complex and long-term solution.
Decentralized Identity: A Glimpse into the Future?
Looking further ahead, decentralized identity systems offer an intriguing possibility. Decentralized identity approaches enable users to control and selectively share their data with service providers and other third parties through verifiable credentials, theoretically eliminating the need for centralized authentication. This approach aligns well with the goals of personal AI agents, empowering users with granular control and principal authority over their digital identities and interactions. While still in its early stages, decentralized identity technology holds some potential for shaping the future of consumer AI agents. However, the novel technologies and consequent switching costs for all the parties involved—especially online merchants and other organizations the consumer wishes to interact with—would be considerable. Therefore, while promising, this remains a more speculative and longer-term potential path that calls for continued innovation and collaboration.
Ultimately, these three design paths offer varying levels and pathways of control, security, and functionality. The decision on which path to adopt will depend heavily on the use case, user expectations, and industry acceptance. While the Full Authentication path is practical for quick adoption and basic tasks, the Intermediary path offers a higher level of security and compliance at the cost of complexity, while Decentralized Identity remains, for the moment, even more complex and speculative. Continued research and development are crucial to address the inherent challenges of each path and unlock the full potential of consumer AI agents.
The Future of Personal AI Agents: Reimagining Consumer Empowerment
The implications of LLM agents for consumer empowerment are profound. If built with the right legal and technical safeguards, they could shift the balance of power, allowing individuals to navigate complex systems—whether financial, commercial, legal, or social—with an AI working solely in their interests. These agents could help consumers make informed choices, protect their privacy, and advocate for their needs in previously impossible ways.
The existing legal framework, starting with UETA, provides a solid foundation on which to build. By leveraging this legal basis and focusing on designing AI agents that align with consumer interests, we can create technologies that empower consumers, giving them tools to engage in the digital world with confidence and consumer-directed autonomy. Understanding this legal foundation allows us to explore how AI agents can be designed to prioritize the consumer’s interests.
Personal AI agents represent a significant shift in how consumers can interact with digital services. By leveraging existing legal frameworks like UETA and focusing on consumer-centric design, we can create AI systems that truly empower individuals. As we move forward, collaboration among technologists, legal experts, policymakers, and consumer advocates will be crucial to ensure these agents are developed securely, reliably, and responsibly. The potential for personal AI agents to level the playing field for consumers in the digital landscape is immense, making this an exciting and important area for continued innovation and development.
In the next blog post, I will delve deeper into how fiduciary duties, especially the duty of loyalty, could serve as a powerful model for AI agents acting and transacting in the interest of consumers, distinct from the interests of merchants and other counterparties to transactions.
