by Dazza Greenwood, Codex Affiliate (1) and Diana Stern

In the first two parts of this series, we explored the emergence of AI agents in everyday transactions and the legal risks they pose, particularly concerning agency and liability. We then examined the potential for AI agent errors and the crucial role of user trust. Now, in this final installment, we turn our attention to proactive solutions and “legal hacks” – innovative strategies to embed legal safeguards directly into AI agent systems, minimizing risk and maximizing their transformative potential. (Here are parts one and two of this series.)

Starting Off on the Right Foot

A robust approach to managing AI agents begins with a clear delegation and consent framework, mirroring established protocols in banking where explicit authorization is required for specific transactions. Just as a bank requires explicit authorization for financial actions, users should grant AI agent providers clearly defined authority from the outset. This is not merely a matter of convenience; it’s a fundamental principle of agency law. 

An emerging consideration for managing AI agent risks is the potential role of insurance products. Just as professional errors and omissions policies protect human professionals, specialized insurance could provide a valuable safety net for autonomous AI transactions. These products could offer protection for consumers and platforms when AI agents encounter unexpected scenarios or make unintended decisions.

A well-defined scope of authority is crucial because, under agency law, the principal (the user) is bound by the agent’s actions within that scope. This minimizes the risk of unintended legal consequences and establishes a clear audit trail if issues arise. We encourage companies to consider the tradeoffs of taking an agency or independent contractor approach, which we touched on in our first post. In addition, companies might try to take the position that users themselves are taking all of the actions, and the AI agent is only providing access and infrastructure.

The optimal time to address legal considerations is during the transaction itself – when the AI agent interacts with a seller or counterparty. This is when agreements are formed, terms are established, and responsibilities are defined. While future AI agents might autonomously negotiate aspects of these agreements, a more immediate and powerful solution is the development of standardized transactional terms, analogous to Creative Commons licenses. Imagine a shared library of legal terms, pre-approved and readily understandable by both humans and AI agents. These standardized terms could provide a common framework for AI-driven transactions, ensuring a shared understanding of rights and obligations between the agent, the user, and the counterparty, streamlining legal interactions at scale.

The Human in the Loop: A Well-Intentioned Speed Bump

Traditionally, the answer to risky AI behavior has been to keep a human “in the loop”. While this provides a critical safety net, it also introduces friction and delays. Moreover, many users barely skim, let alone fully comprehend, lengthy terms of service before clicking “I Agree.”

While human oversight remains a necessary precaution in the current stage of AI agent development, particularly for high-value or complex transactions, the ultimate goal is to create agents that can operate autonomously and reliably, with minimal human intervention. Consider a practical scenario: an AI travel booking agent that could autonomously negotiate flexible cancellation policies with service providers based on predefined user preferences. For instance, the agent might secure more lenient terms for a trip to Paris, adapting the booking conditions to match the user’s specific risk tolerance and travel plans. Users could set preferences once and have each new AI agent they use incorporate them.

The traditional approach of “human in the loop,” while providing a safety net, significantly reduces the efficiency and scalability that make AI agents so compelling. Furthermore, the effectiveness of human oversight is questionable, especially when users often accept complex terms of service without careful review. To move beyond these limitations and fully realize the potential of AI agents, we need to explore proactive strategies – “legal hacks” – to embed legal safeguards directly into their design and operation.

Legal Hacks for AI Agents: Addressing What Could Go Wrong

To move beyond the limitations of human oversight and address the inherent legal risks of AI agents, we now explore “legal hacks” – proactive strategies to embed legal safeguards directly into the design and operation of these systems. These “legal hacks” are not about circumventing the law, but rather about leveraging technology to make legal compliance more efficient, reliable, and scalable. Our aim is to create more predictable legal outcomes, reduce reliance on cumbersome human intervention, and potentially offer first-mover advantages to companies that adopt these innovative approaches.

Teaching AI to Read the Fine Print

One powerful “legal hack” is to integrate relevant contractual terms directly into the AI agent’s decision-making process. Instead of treating legal agreements as external constraints, we can make them an integral part of the agent’s operational logic. This could involve platforms providing terms of service in structured, machine-readable formats, potentially via APIs or standardized data formats. AI agents could then be designed to parse this structured legal data, proactively assess potential compliance issues before executing transactions, and ensure alignment with applicable terms. An innovative approach to managing evolving legal terms could involve a broadcast mechanism. When platform terms of service are updated, AI agents could receive immediate notifications, eliminating the need for constant manual checking. This would allow agents to stay continuously aligned with the latest legal requirements without computational overhead.

Designing for Compliance: Checkpoints and Balances

This compliance-centric approach requires embedding checkpoints within the AI agent’s workflow. Before executing a transaction, the agent would cross-reference its planned actions against applicable legal terms, flagging potential non-compliance and, if necessary, prompting human review or adjusting its course of action. This creates a system of internal controls, ensuring that the agent operates within defined legal boundaries.

The Devil in the Details: Challenges and Considerations

Implementing this approach is not without challenges. Terms of service are often lengthy, complex, and ambiguous. Teaching an AI to interpret and apply these terms requires sophisticated natural language processing and a deep understanding of legal principles. Furthermore, we must be mindful of the unauthorized practice of law (UPL). If an AI agent were to directly advise users about complex legal terms or offer legal interpretations, it could potentially be construed as UPL. One way to mitigate this risk is to design these compliance tools primarily for the benefit of the AI agent provider. By focusing on internal compliance checks and business rule enforcement, the tool helps the provider ensure the AI operates within legal boundaries, while the AI agent itself communicates only business restrictions or options to the user, rather than direct legal advice.

The Future: AI-Friendly Terms of Service

Looking ahead, we envision a future where terms of service are designed specifically for AI comprehension. Platforms could create computational versions of their terms, optimized for machine readability while maintaining legal validity. This could involve a standardized format, perhaps analogous to the ‘robots.txt’ file that web crawlers use to understand website rules. In fact, today, AI agent developers are already updating business websites to ensure they are easily readable by LLMs and AI agents by providing a plain text version of the information. The ‘LLMS.txt’ specification is the main way people are doing this. A website’s terms of service could be put into LLMs.txt format today, making this legal hack immediately and easily achievable. In the future, an LLMS.txt file could provide additional legal and compliance requirements for AI agents operating on a given platform, making legal expectations clear and accessible. Furthermore, extending attribution fields, similar to those in some AI APIs like Google Gemini that are used to cite sources, to include metadata identifying the responsible party for an AI agent’s actions would enhance transparency and accountability in AI-driven transactions. Taking it even further, in the future, these machine-readable terms of service could roll up into immediately understandable summaries for end users who might want to filter by, for example, AI agents that act as a legal agent (as opposed to those that take the alternative independent contractor or infrastructure approaches referenced above).

On the Horizon: Leveraging Zero Knowledge Proofs

Another groundbreaking “legal hack,” particularly relevant to addressing privacy concerns highlighted in our second post, lies in the realm of cryptography: zero-knowledge proofs. A zero-knowledge proof is a cryptographic method that allows one party (the prover) to convince another party (the verifier) that a statement is true, without revealing any information beyond the validity of the statement itself. Imagine you have a magic door that only opens if you know a secret password. You want to prove to someone that you know the password without actually telling them what it is. A zero-knowledge proof would allow you to do just that. You could interact with the door in a way that demonstrates you can open it, convincing the other person you know the secret without ever revealing the password itself.

In the context of AI agents, zero-knowledge proofs could enable agents to process sensitive data – such as personal information required for a purchase – without actually revealing that data to the agent itself, the platform, or other parties. This significantly enhances user privacy and reduces the risk of data breaches, key considerations highlighted by privacy regulations. For AI agent providers, incorporating zero-knowledge proofs could minimize the amount of sensitive data they collect, simplifying compliance with privacy regulations.

Conclusion: Code as Law 2.0 – Architecting the Digital Future

Companies that pioneer these “legal hacks” – from AI-readable terms of service and standardized transactional terms to compliance checkpoints and zero-knowledge proofs – are not simply adapting to a changing legal landscape; they are actively shaping it. These innovations represent a fusion of law and code, creating a “Code as Law 2.0” paradigm that has the potential to revolutionize digital interactions. By embedding legal safeguards directly into AI agents, we can reduce compliance costs, mitigate legal risks, enhance user trust, and unlock new global markets. As AI agents become increasingly sophisticated and autonomous, embracing these proactive legal strategies will be essential for responsible innovation and building a more trustworthy, efficient, and equitable digital future. The question is not if the industry will adopt AI agents for transactions, but how quickly will you adapt to this emerging future and gain advantage over those who lag behind?

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(1) Dazza Greenwood runs Civics.Com consultancy services, and he founded and leads law.MIT.edu and heads the Agentic GenAI Transaction Systems research project at Stanford’s CodeX. Diana Stern is Deputy General Counsel at Protocol Labs, Inc. and Special Counsel at DLx Law.

 

Thanks to Sarah Conley Odenkirk, art attorney and founder of ArtConverge, and Jessy Kate Schingler, Law Clerk, Mill Law Center and Earth Law Center, for their valuable feedback on this post.