AI agents are rapidly becoming the front door to commerce. Whether it’s ChatGPT Agent helping consumers navigate purchasing decisions, Perplexity surfacing product recommendations, or Alexa+ managing household orders, these systems are positioning themselves to mediate how hundreds of millions of consumers choose, purchase, and interact with brands.
This transformation presents a critical inflection point. Will these agents genuinely serve consumers’ best interests, or will they become sophisticated sales funnels that prioritize profit over user needs? The answer depends on the technical standards, legal frameworks, and alignment mechanisms we build today.
Just two weeks ago, Consumer Reports, Stanford’s Digital Economy Lab and CodeX brought together researchers, policy leaders and technologists from organizations including Anthropic, Microsoft, OpenAI, Salesforce, SAP, Visa, and Omidyar Networks to work on addressing this challenge – together.
From Vision to Prototypes
This was our second Loyal Agents Workshop. The first, held earlier this year, surfaced some of the foundational questions: How will consumers authorize agents to act on their behalf? Who is responsible when agents make mistakes? How do we safeguard privacy and ensure consumers have meaningful control?
That initial gathering made clear the need for reference patterns and best practices—and for quickly moving from theory to practice through prototyping some foundational use cases. Since then, our CR & Stanford research collaboration has become more formalized as we’ve begun testing patterns and laying the groundwork for a trusted ecosystem of consumer-authorized agents in partnership with AI leaders across industry.
The second workshop, held in Palo Alto, was a chance to take the next step. As Inflection AI’s Chief Product Officer Ian McCarthy reflected, it was “a timely gathering of inspiring, expert perspectives to discuss this crucial theme as the focus of attention on agentic AI shifts from RPA++ in the enterprise to profound transformations of the consumer internet.“
Three Research Questions, Key Takeaways
The Consumer Reports-Stanford research collaboration centers on three fundamental questions that will determine whether the emerging agent marketplace serves consumers or exploits them:
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- How can AI agents transact securely on behalf of consumers?
- What fiduciary and other legal duties should agents owe their users?
- How do we ensure agents are aligned with consumer preferences?
Rather than treating these as abstract research problems, the workshop participants rolled up their sleeves to develop actionable next steps.
Authorization Primitives for Non-Deterministic Systems
The first breakout group tackled a fundamental challenge: giving agents a common “language” for acting on consumers’ behalf. Generative AI systems are inherently non-deterministic, but current authorization standards like OAuth and OIDC were built for predictable, deterministic systems.
Drawing explicit parallels to the DevOps movement, the group committed to developing “authorization primitives” that balance agent autonomy with security and human oversight. The technical approach builds on proven standards while adapting them for AI’s probabilistic nature.
Evaluating Agent Trustworthiness and Accountability
The second group explored tensions around appropriate legal frameworks and responsibilities for AI agents, particularly how to configure different levels of autonomy while maintaining accountability and consumer trust. Four critical tracks emerged: conceptualizing what trust means for agents, approaches to signalling the trustworthiness of agents, developing evaluation methodologies using “LLM-as-a-Judge” approaches, and defining operating environments for testing and deployment.
User-Centric Preference Architecture
The third breakout tackled the most fundamentally important challenge: ensuring agents genuinely serve their users rather than manipulating them. Their interdisciplinary approach emphasized grounding technical solutions in deep understanding of human psychology and decision-making.
The group identified three core themes: intuitive preference elicitation, consumer literacy and social norms, and psychological foundations drawn from social psychology and economics literature. Their concrete commitments include benchmarking methods for preference alignment, evaluation frameworks, multi-agent preference integration systems, data portability tools, and transparency mechanisms. There’s interest in bringing forth proposals for an open and user-centric “preference architecture,” potentially based on the proposed Human Context Protocol (HCP).
What’s Next
For those working in consumer protection and technology policy, this collaboration represents something crucial: proactive standard-setting before harmful practices become entrenched. Too often, we find ourselves playing catch-up, trying to regulate technologies after they’ve already reshaped markets in problematic ways.
We’re at a rare moment where the technical architecture, legal frameworks, and business practices around AI agents are still being defined. The choices made in the next year will determine whether these powerful systems amplify consumer agency or undermine it.
Visit loyalagents.org to learn more about the collaboration and drop us a line if you’d like to get involved. We’ve set up this site to serve as a front door and evolving repository for this initiative. We look forward to hearing from you.
