AI Agents vs. Fiduciary Duty: The Trillion-Dollar Consent Bottleneck

By: Ira Rothken

Why autonomous AI-binding companies and making automated legal decisions (ALD) without informed oversight create a board-level fiduciary duty governance crisis—and how Law Engineers (lawyers who code and monitor) and governance-as-code enable safer, machine-scale commerce.

If you are an officer, board member, or lawyer for a company using AI or "automated legal decision-making" (ALD), then take this 30-second quiz. If you answer any of the questions as "yes," then read on. This article is for you, and it's urgent.

Red Flags: Are You Exposed?

If your organization can check multiple of these boxes, you have significant undocumented AI agent liability:

1. AI agents and ALD have access to payment credentials, email, or API keys

2. No attorney has reviewed the decision logic, guardrails, or heuristics governing AI agent or ALD behavior

3. You cannot produce a list of contract terms your AI agents or ALD have accepted in the past 90 days

4. There are no materiality thresholds requiring human escalation before AI or ALD accepts terms

5. The board has not discussed AI agent contracting and ALD legal risks in the past year

6. Your AI agents and ALD operate 24/7 without human monitoring of their decisions

7. You have no audit trail of which AI agent and ALD accepted which terms when

8. Software engineers, not attorneys, designed the logic for what terms AI agents and ALD can accept

9. Your D&O insurance application doesn't mention AI agent or ALD deployment

10. You cannot explain how your AI agent or ALD deployment complies with UETA Section 10's error-correction requirement

Even one checked box represents governance exposure. Multiple boxes indicate the kind of systematic oversight failure that fiduciary duty was designed to address.

Originally published on Linkedin here.

Introduction

The agentic commerce revolution is poised to transform over a trillion dollars in global transactions. Visa completed hundreds of AI agent transactions in late 2025 and declared that "2025 will be the final year consumers shop and checkout alone." Mastercard launched Agent Pay and enabled all U.S. cardholders before the 2025 holiday season. Platforms like OpenClaw are putting autonomous AI agents into the hands of thousands of entrepreneurs and small businesses.

But there's a critical AI agent bottleneck problem no one wants to confront: we're witnessing the erosion of informed consent. In some case we’re witnessing the death of informed consent.

AI agents are making legally binding decisions at machine speed—clicking "I Agree" to terms no human has read, accepting contract provisions no lawyer has reviewed, and binding organizations to obligations no board has approved. The legal fiction that underpins centuries of contract law—that parties knowingly agree to terms—has quietly collapsed. Unless addressed, this will have a chilling effect on payment processors' involvement and the growth of AI agent commerce.

The AI agents engage in legally risky conduct, the most obvious being visiting sites that prohibit bots and automated agents in their terms of use.

In AI agent transactions, humans often are not engaging with a website's UI or UX, or with a registration path, that is carefully controlled to meet FTC standards for disclosure, notice, and informed consent. AI Agents or bots are currently not a good substitute for making binding legal decisions for humans.

This isn't just a technical challenge. It's a crisis in legal governance, fiduciary duty, and consumer protection that threatens to turn the trillion-dollar opportunity into a litigation disaster—and it stems from a 1999 electronic commerce law applied to 2026 probabilistic AI systems that could hold you liable for the acts of your AI agents.

The 1999 UETA Law / 2026 AI Mismatch: Why Responsibility Without Control Creates Liability

The legal foundation for AI agent transactions rests on laws designed for a simpler world, creating a dangerous gap between what the law assumes and what AI agents actually do.

The Uniform Electronic Transactions Act (UETA), adopted by 49 states and the District of Columbia, provides the core framework. The federal Electronic Signatures in Global and National Commerce Act (E-SIGN) supplements UETA by giving a parallel framework for interstate and international transactions. New York, the only state that hasn't adopted the UETA, has its own Electronic Signatures and Records Act (ESRA), which is functionally similar. Together, these laws effectively cover the entire United States.

But here's the problem: the UETA legal frameworks were drafted in 1999-2000 under an implicit assumption of bounded, predictable automation — not probabilistic AI systems capable of novel legal judgment—think shopping carts that execute pre-programmed rules.

Here's the fundamental tension: The law is right to hold you responsible for your AI agent's decisions—you deployed it, you gave it authority, you should own the consequences. But the law was written on the assumption that you had meaningful control over what your electronic agent would do.

The law assumed that if you deployed an electronic agent, you programmed it to do exactly what you wanted. For a 1999 shopping cart, that was true—you had complete control over its behavior. For a 2026 AI agent making purchasing or web signup decisions, it's not. You might direct your AI to "negotiate favorable terms." Still, you haven't controlled which indemnification clauses it accepts, when liability caps are too low, or whether a specific arbitration provision creates unacceptable risk.

We provide guidance on solving this paradox below. We will discuss the “Swarm Contract Protocol” and “Law Engineers.” But first, more on the state of the legal risks.

The UETA Binding Mechanism—And Its AI Trap

Under UETA Section 14, contracts formed by electronic agents are legally binding "even if no individual was aware of or reviewed the electronic agents' actions or the resulting terms and agreement." The commentary explains that the "requisite intention flows from the programming and use of the machine."

This made sense for a 1999 shopping cart programmed to buy precisely what you put in it. It provides massive exposure when applied to 2026 semi-autonomous AI agents that either don't comprehensively analyze the legal consequences of actions, like visiting sites that prohibit automated agents and bots, or use half-baked probabilistic reasoning to navigate complex legal decisions.

The E-SIGN Act (15 U.S.C. § 7001) reinforces this framework at the federal level, ensuring that electronic signatures and records are legally valid in interstate commerce. The problem is identical: what was designed for simple automated confirmations now governs sophisticated AI agents making complex contractual decisions.

The Narrow Error-Correction Protection—And Why It May Not Apply

UETA Section 10 provides one limited safety valve: individuals may avoid erroneous transactions if the electronic agent "did not provide an opportunity for the prevention or correction of the error." This provision cannot be waived by contract—it's a fundamental protection.

But here's the critical question courts haven't yet addressed: If you deliberately configured your AI to operate fully autonomously without human confirmation points, have you eliminated the very decision points where error prevention would operate?

Section 10 was designed for situations such as a human entering a data-entry error while using a deterministic system—typing "1000" instead of "100." The law assumes there should be a review screen, a confirmation button, and a chance to catch the error before you're bound.

AI agents operating in fully autonomous mode may eliminate all those decision points. You set the agent's parameters, provide credentials, and let it transact independently. In many instances, you allow the AI agent to violate third-party websites’ terms of use that prohibit the use of automated agents or bots. Days or weeks later, you discover it accepted unfavorable terms or made decisions you wouldn't have approved.

Did the agent provide an "opportunity for prevention or correction"? Or did your architectural choice to deploy fully autonomous operation waive or interfere with that protection? This is an open question that organizations should notassume will be resolved in their favor. The law is murky, and organizations are deploying these systems without knowing the answer.

Why This Matters

You're generally legally responsible for AI agent decisions you haven't architected controls for.

This isn't an argument against legal responsibility—it's an argument that responsibility requires governance. If the law holds you 100% accountable for your AI agent's decisions (and it should), then you need architectural controls that give you meaningful oversight over those legal decisions before they're made.

The Erosion of Informed Consent

Here's what's happening right now: AI agents are making legally binding decisions at machine speed, putting companies at risk. The gap between what AI agents can do and what the law requires creates significant exposure for every organization that deploys them.

Consider the real-world failures already emerging:

The $365,000 EEOC Settlement: iTutorGroup deployed automated hiring software that rejected female applicants over 55 and male applicants over 60. This was a "legal decision"—age-based filtering—embedded in code without attorney oversight. The result: a direct violation of the Age Discrimination in Employment Act and a six-figure settlement.

The Air Canada Matter: In 2024, a tribunal rejected the airline's argument that its chatbot was a separate legal entity. The court held Air Canada liable for its agent's "negligent misrepresentation," establishing a critical precedent: any legal commitment made by your AI agent is a binding act that you, the principal, are responsible for – even if you did not intend it.

The OpenClaw Account Creation Spree: Autonomous agents, given broad directives to "establish social media presence" or "create accounts on relevant platforms," have been documented making errors at scale—creating 500 social media accounts instead of one, signing up for dozens of subscription services simultaneously, and accepting terms of service across platforms without any consistency or legal review. Under the legal frameworks described above, because the owner authorized the agent's access and configured its behavior, they likely bear full responsibility for every account created and every terms-of-service agreement accepted. "My AI misunderstood the instruction" is unlikely to be a reliable legal defense.

These aren't edge cases. They're warning signs of a structural problem.

The Law of Guardrails: A Framework Predicted

This crisis isn't entirely unexpected. In March 2024, I published "AI Law for Innovators: Navigating the 'Law of Guardrails' and 'Ethical Bias,'" which predicted that "AI Law is Quickly Becoming the Law of Guardrails" and that "Savvy tech lawyers are needed to play a crucial role in the evolution of AI especially when it comes to AI LLMs by using software code, and soon 'no code' systems, to implement compliance by code design."

https://www.linkedin.com/pulse/ai-law-innovators-navigating-guardrails-ethical-bias-ira-rothken-3zenc/

That article explored how guardrails function as "compliance firewalls" for AI systems—a concept that has become critical as AI agents moved from generating text to executing transactions. The framework distinguished between "Macro-Guardrails" (built into AI platforms) and "Micro-Guardrails" (implemented by organizations via API calls and prompts to ensure compliance with specific legal, ethical, and business requirements).

The article emphasized that "Lawyers will need to help develop custom guardrails using legal-tech and reg-tech for each client's use case," covering data privacy, intellectual property rights, and transparency. It called for collaborative efforts in which lawyers work alongside engineers to embed these compliance mechanisms into code.

What has changed between March 2024 and today is the scale and stakes. The guardrails framework I described was designed primarily for AI systems generating outputs—text, analysis, and recommendations. Now, with agentic commerce, AI systems are making decisions with immediate legal effect: accepting contract terms, creating binding obligations, transferring rights.

The principle remains the same: legal judgment must be embedded at the code level, not applied as an afterthought. But the implementation requires a new professional category. The "savvy tech lawyers" I described in 2024 needed to understand how to guide guardrail design. Law Engineers in 2026 must be able to architect and write the guardrails themselves, bearing professional responsibility for the legal logic embedded in autonomous decision-making systems.

The "Law of Guardrails" predicted the framework. But as AI agents evolved from generating text to executing binding transactions, the challenge became even more acute. Here's why:

Why 85.8% Accuracy Creates 100% Liability

The instinctive response is: "Make the AI better at reading contracts." But this fundamentally misunderstands the problem.

Large Language Models are probabilistic systems—they generate outputs based on statistical predictions, not fixed rules. In computer science, a mid-80 % accuracy rate is considered decent performance. In law, that approximate 15% error rate across thousands of transactions represents catastrophic exposure.

A hypothetical illustrates the stakes: A mid-sized manufacturer deploys procurement AI to handle supply agreements. In one day, the AI processes 847 purchase orders, negotiates terms, and commits $23 million in obligations. Weeks later, attorneys audit the contracts and discover that 120 of them contain indemnification clauses exposing the company to unlimited liability for product defects—provisions no human lawyer would approve.

The AI's accuracy? 85.8%—an arguable figure consistent with some documented accuracy rates for LLMs on legal analysis tasks requiring judgment and interpretation.

For the board, this was not informed consent; those 120 failures could cost more than all 833 successes combined. This is why technical improvements alone cannot solve the problem. As long as the system remains probabilistic, it cannot guarantee the deterministic reliability required by legal accountability. This is an example of the AI agent legal roadblock.

The Board-Level Crisis: Fiduciary Duty Meets Autonomous Systems

California AB 316: The "Autonomous AI" Defense Is Gone

Effective January 1, 2026, in covered civil actions, California Civil Code § 1714.46 provides that defendants cannot disclaim liability by pointing to autonomous AI behavior. In other words, you cannot deploy AI agents and then disclaim responsibility by pointing to the system's independence. The law does not eliminate all defenses, but it forecloses the argument that “the AI did it, not us.” If it hacks, you may be liable. If it infringes, you may be liable. If it violates payment processing laws, you may be liable.

The EU's Gold Standard: Mandatory Human Oversight and Strict Liability

For organizations operating globally, the European Union has established requirements that make Law Engineers essentially mandatory rather than optional.

The EU AI Act (Article 14) requires that "high-risk" AI systems—including autonomous financial and contractual tools—be designed for effective human oversight. Specifically, the individuals assigned to oversight must be able to:

1. Interpret the output - Understand the reasoning behind an agent's contractual and other legal decisions

2. Override or reverse - Disregard the AI's decision in real-time

3. Intervene with a kill switch - Stop the system entirely through a safe procedure

A standard software engineer cannot fulfill Article 14's requirement to "interpret" legal outputs. This role requires someone who understands both the underlying legal heuristics, as a lawyer, and the technical architecture—a Law Engineer.

The Product Liability Directive (PLD), coming into full effect by December 2026, introduces strict liability (no-fault liability) for AI systems by explicitly classifying them as "products." If an AI agent's decision logic is deemed "defective"—meaning it does not provide the safety or legal certainty a person is entitled to expect—the deployer can be held liable for harm without proving negligence.

In this strict liability regime, the Law Engineer serves as the primary risk mitigator, ensuring that governance-as-code prevents the logic defects that lead to liability claims. For U.S. companies serving EU customers or operating EU subsidiaries, these aren't foreign requirements—they're operational mandates that require Law Engineers to meet.

Fiduciary Duties and the Oversight Failure

The deployment of AI agents without proper legal governance creates exposure across multiple fiduciary duty frameworks:

The Caremark Duty of Oversight (Board Level)

Under Delaware law's Caremark doctrine, directors owe a duty to implement reasonable information and reporting systems to monitor material risks. Deploying AI agents with contracting authority but without legal oversight mechanisms creates a documented governance gap that can trigger liability.

Officers' Duty of Care and Oversight

Corporate officers—CEOs, CFOs, COOs, General Counsel—owe fiduciary duties distinct from and in addition to board duties. Under Delaware law and most state corporate codes, officers have a duty to act in good faith, with the care that an ordinarily prudent person would exercise under similar circumstances, and in the best interests of the corporation.

Deploying AI agents that make legal decisions without implementing attorney oversight at the code level is difficult to characterize as "ordinarily prudent" when:

1. The technology is probabilistic and inherently unreliable for legal judgments.

2. Existing legal frameworks (UETA, professional responsibility rules) create known exposure.

3. The officer knows (or should know) that AI agents are binding the company to unreviewed terms.

4. Industry standards (e.g., NIST and the EU AI Act) establish governance expectations.

Professional Responsibility: ABA Model Rule 5.3

General Counsel face their own exposure. ABA Model Rule 5.3 requires attorneys to ensure that non-lawyer "assistance"—which in 2026 includes automated systems—is compatible with professional obligations. If a GC permits a system to execute contracts with embedded legal decision-making logic that no attorney properly reviewed, they aren't just making a business mistake. They may be violating their ethical duty to supervise the "agents" acting on the organization's behalf.

The Business Judgment Rule Does Not Protect Uninformed Decisions

The Business Judgment Rule protects directors and officers when decisions are informed, made in good faith, and in the corporation's best interests. Deploying AI agents without mechanisms for legal review of accepted terms is difficult to characterize as an "informed" decision when:

1. Management cannot explain what terms the AI is authorized to accept

2. No reporting system exists to monitor what contracts have been agreed to

3. No attorney reviewed the decision logic governing AI behavior

4. The board was not presented with an analysis of the legal risks

The Uncomfortable Questions Decision-Makers Must Answer:

For Directors:

1. What reporting system alerts you to contract terms your AI agents have accepted?

2. How do you satisfy your oversight duty for risks you're not monitoring?

3. Can you demonstrate informed decision-making about AI deployment risks?

For Officers:

1. What compliance mechanism verifies that AI-accepted terms align with corporate policy?

2. How do you monitor what your AI agents are agreeing to across potentially thousands of transactions?

3. Can you demonstrate that deploying AI without legal oversight was an ordinarily prudent decision?

For General Counsel:

1. Under ABA Model Rule 5.3, how are you supervising AI systems that perform legal functions?

2. Under Rule 1.13, have you reported to the board that AI contracting creates a material risk?

3. Can you defend permitting non-attorneys to write legal decision-making logic?

How does a company comply with its fiduciary duties in the era of code-based legal decision-making for high-speed AI agentic business transactions?

The Solution: Law Engineers—Attorneys Who Code and Monitor Legal Decision-making in AI Agents

The answer is not to abandon agentic commerce or slow it down. The answer is to build legal intelligence into these systems from the start through a new professional technology lawyer category: Law Engineers.

What Are Law Engineers?

This is fundamentally different from existing roles:

1. Legal Engineers optimize workflows for human lawyers—contract lifecycle management, legal ops tools

2. Law Engineers architect the decision logic that allows AI agents to operate within legal guardrails autonomously

What Law Engineers Actually Do

1. Design Legal Decision Trees at the Code Level

Law Engineers can decide on the risk-benefit of AI systems, such as when to use probabilistic systems and when to use rule-based systems, and translate legal judgment into executable logic: "If an arbitration clause is present AND a liability cap is below a threshold AND there is no prior negotiation, THEN flag for human review." This means routine, low-risk transactions flow through at full speed while material or risky decisions receive the oversight and fiduciary duties they require.

2. Install Circuit Breakers for High-Risk Terms

AI systems struggle to reliably spot the missing "not" that inverts a liability clause or distinguish between reasonable and unlimited indemnification. Law Engineers preempt these failures by encoding pattern-recognition rules, guardrails, and validation checks—ensuring an attorney reviews terms before the organization is bound, but only when risk profiles warrant it.

3. Build Compliance Architectures

Law Engineers implement systems that generate legally sufficient audit trails, satisfy UETA's error-correction requirements, create reporting mechanisms that meet Caremark oversight duties, and provide the board visibility without disrupting transaction flow.

4. Bear Professional Responsibility

Unlike programmers, Law Engineers are licensed lawyers subject to professional responsibility rules, and in specific contexts, communications with them are privileged. This gives boards the accountability and protection chain they need to demonstrate informed, good-faith governance.

Solving the Bottleneck Creates Competitive Advantage

Organizations instinctively see legal oversight as friction that destroys the speed advantage of agentic commerce. But this fundamentally misunderstands the strategic landscape.

The bottleneck exists whether you address it or not. The choice is between:

1. Deploying without governance and discovering the bottleneck when litigation, regulatory action, or catastrophic contract failures force you to retrofit legal oversight—at maximum cost and minimum effectiveness

2. Building governance into the architecture and converting legal compliance from a liability into a market differentiator

Done right, Law Engineers don't slow the system down. They make it trustworthy, which is a precondition for deploying it at scale.

Here's the competitive advantage: An organization that can demonstrate its AI agents operate within legally architected guardrails can:

1. Scale with confidence rather than scaling with undocumented liability

2. Win enterprise customers who demand governance before they'll integrate with your agents

3. Avoid the coming wave of litigation that will hit competitors who deploy without proper oversight

4. Meet evolving regulatory requirements (EU AI Act, California AB 316, NIST standards) from day one

5. Attract capital from investors who understand governance risk

6. Defend against D&O insurance challenges with documented evidence of informed oversight

The organizations building governance foundations now aren't sacrificing speed—they're building the trust infrastructure that will allow them to operate at speeds their competitors can't match without taking on risks their boards won't accept.

Red Flags: Are You Exposed?

If your organization can check multiple of these boxes, you have significant undocumented AI agent liability:

1. AI agents and ALD have access to payment credentials, email, or API keys

2. No attorney has reviewed the decision logic, guardrails, or heuristics governing AI agent or ALD behavior

3. You cannot produce a list of contract terms your AI agents or ALD have accepted in the past 90 days

4. There are no materiality thresholds requiring human escalation before AI or ALD accepts terms

5. The board has not discussed AI agent contracting and ALD legal risks in the past year

6. Your AI agents and ALD operate 24/7 without human monitoring of their decisions

7. You have no audit trail of which AI agent and ALD accepted which terms when

8. Software engineers, not attorneys, designed the logic for what terms AI agents and ALD can accept

9. Your D&O insurance application doesn't mention AI agent or ALD deployment

10. You cannot explain how your AI agent or ALD deployment complies with UETA Section 10's error-correction requirement

Even one checked box represents governance exposure. Multiple boxes indicate the kind of systematic oversight failure that fiduciary duty was designed to address.

What This Means for Different Stakeholders

For Board Members:

1. Caremark Exposure: Deploying AI agents without oversight mechanisms creates documented governance gaps

2. D&O Insurance Risk: Coverage may not extend to AI-related claims; expect underwriting scrutiny

3. Fiduciary Duty: You cannot satisfy the duty to be "informed" about material decisions if AI agents are accepting contracts you're not monitoring

4. Personal Liability: When derivative suits come, the question will be: "Why didn't the board implement basic oversight?"

For General Counsel:

1. Professional Responsibility: ABA Model Rule 5.3 requires supervision of non-lawyer "assistance"—including AI systems.

2. Exposure: Permitting legal decision-making logic to be written without attorney involvement may violate professional obligations

3. Reporting Duty: Under Rule 1.13, you must report to the board when management actions threaten substantial harm

4. The Question: If you know AI agents are binding the company without proper legal review, and you haven't escalated, are you complicit?

For Chief Executive Officers:

1. Competitive Advantage vs. Litigation Risk: First movers with proper governance will define the market; late movers will defend lawsuits

2. Enterprise Customer Requirements: B2B customers increasingly demand AI governance documentation before integration

3. Regulatory Scrutiny: FTC, CFPB, EU regulators are watching algorithmic decision-making—prepare for enforcement

4. Capital Access: Institutional investors are adding AI governance to due diligence checklists

For Chief Technology Officers:

1. The Attribution Problem: When AI makes a bad decision, "the software engineer didn't know it was a legal issue" won't shield the organization

2. Technical Debt: Retrofitting legal guardrails and logic into deployed systems is exponentially more expensive than building them in

3. Vendor Relationships: Payment networks (Visa, Mastercard) are building authentication protocols that assume legal governance exists

4. Talent Gap: You need professionals who understand both code and law—neither traditional developers nor traditional attorneys can architect this alone

For Investors and Private Equity:

1. Due Diligence Flag: AI agent deployment without Law Engineer oversight is a governance red flag

2. Portfolio Risk: Companies using AI agents without documented legal oversight carry hidden liability

3. Value Creation Opportunity: Implementing Law Engineer frameworks increases enterprise value and exit multiples

4. Regulatory Risk: EU AI Act, California AB 316, and emerging regulations make governance a compliance requirement, not a best practice.

The Coming Reckoning

When the first major bankruptcy or securities fraud case reveals a company bound to hundreds of unfavorable contracts its AI silently accepted, the governance failure will be evident in hindsight:

1. Board minutes showing no discussion of AI deployment, agent, and contracting risks

2. No reporting systems monitoring agent conduct and accepted terms

3. No legal oversight of automated legal decision-making architecture

4. No Law Engineers involved in AI and agent system design

5. Just a documented decision to deploy AI and autonomous systems and hope for the best

Shareholder derivative suits will write themselves. D&O insurers will challenge coverage. Bankruptcy examiners will detail the oversight failures. And boards will have no answer for why they deployed systems capable of binding the company to material contracts without implementing the legal oversight their fiduciary duties clearly required.

Emerging Solutions: The Path Forward (But Not Yet Fully Here)

Potential technical solutions show promise, but until they can mature and get integrated into code at scale, the trillion-dollar bottleneck remains. The fundamental challenge: implementing these emerging frameworks requires professionals who understand both law and code—precisely what Law Engineers would provide, if organizations actually deployed them.

NIST Standards: Emerging Government Benchmarks

The National Institute of Standards and Technology (NIST) has begun establishing standards for trustworthy AI systems. While adoption is still early, NIST's guidance is likely to become the technical benchmark for what constitutes "reasonable" AI governance in litigation:

The AI Risk Management Framework (AI RMF) establishes that trustworthy AI systems must be valid, reliable, and safe. For agentic commerce, this means AI agents should operate within deterministic boundaries—exactly what Law Engineers would architect.

The Cybersecurity Framework AI Profile (NISTIR 8596) maps cybersecurity requirements to AI systems, establishing that securing AI system components—including the legal decision logic embedded in agent prompts and guardrails—should be a foundational requirement for operational trust.

These frameworks are emerging standards of care. When litigation asks, "Did you implement reasonable AI governance?" courts will likely look to NIST frameworks. Organizations without Law Engineers architecting compliant systems may struggle to demonstrate they met evolving industry standards.

Swarm Contract Protocol: A Promising Framework for Putting Law Into Code

Swarm contracts arise out of work done on the use of Smart Contracts on the Blockchain in conjunction with Ricardian contracts. The practical implementation at scale has been limited by a critical gap: the lack of professionals who can translate legal requirements into machine-executable logic. This is precisely the gap Law Engineers are positioned to fill, making Swarm contracts viable for the first time in commercial practice. While no Swarm Contract like standard has yet reached widespread commercial adoption, the framework illustrates how deterministic legal control can coexist with agentic speed.

The concept creates what could be thought of as "Legal DNA" for transactions by binding three elements together:

1. Legal Prose - The human-readable contract text (the intent)

2. Machine-Readable Parameters - Structured data defining permitted terms (the boundaries)

3. Cryptographic Signature - A hash that binds them together (non-repudiation)

Here's why this concept matters for boards: A properly implemented Swarm contract would allow an AI agent to instantly verify whether proposed terms fall within pre-authorized boundaries, without guessing or probabilistic analysis.

For example, a Law Engineer translating corporate procurement policy into a Swarm contract might specify:

1. Acceptable indemnification: Mutual only; reject unilateral

2. Liability caps: Must exist and exceed transaction value by 2x

3. Arbitration clauses: Reject unless explicitly pre-approved

4. Auto-renewal terms: Flag for review if price escalation exceeds 5% annually

5. IP assignment: Hard stop—always require attorney review

This would be governance-as-code. The legal strategy is embedded in the architecture, not applied as an afterthought.

The problem: Few organizations are actually implementing this approach. The gap between the theoretical framework and deployed reality is where the trillion-dollar bottleneck sits.

Beyond Swarm Contract: The Emerging Infrastructure for Agent-to-Agent Commerce

Swarm Contract solves one critical piece of the agentic commerce puzzle: how AI agents understand and validate the legal terms governing transactions. But understanding terms is only the beginning. The deeper challenge—and the trillion-dollar opportunity—is enabling agents to hire, pay, and trust one another without a human-in-the-loop.

This is a fundamentally more complex problem than "helping consumers shop online with AI assistants." Consumer-facing agents operate within existing infrastructure: they use your credit card, your identity, and your legal standing. Agent-to-agent commerce requires a more robust solution: an autonomous economic layer where agents transact directly with one another at machine speed, with legal and financial accountability.

Across the technology ecosystem, different teams have advanced different corners of this puzzle. Each contribution is significant. But they remain parallel lines—extraordinary engineering work that hasn't yet converged into a functioning autonomous economy.

The Fragmented Landscape

Anthropic's Model Context Protocol (MCP) standardized how AI models access tools and data sources. An agent can now connect to databases, APIs, and services using a standard protocol. This solves the "integration chaos" problem—but it doesn't address what happens when the tool requires payment, or when accessing the data creates a legal obligation.

Google's Agent-to-Agent (A2A) Protocol gave agents a shared language for coordination and negotiation. Agents can now communicate intent, exchange proposals, and coordinate complex workflows. But communication isn't commerce—the protocol doesn't define how agents pay each other, verify identity, or enforce agreements.

Coinbase's x402 Protocol extended HTTP with native payment capabilities, enabling any web request to include cryptocurrency payment. Technically elegant, but it operates in the crypto ecosystem, not the traditional financial system, where most commerce occurs.

Stripe's Agent Commerce Protocol (ACP) made fiat transactions accessible to AI agents, enabling them to initiate payments through traditional banking infrastructure. Critical for mainstream adoption, but payment alone doesn't establish legal accountability—who's liable if the agent pays for services that violate organizational policy?

Ethereum's ERC-8004 set the framework for agent identity and reputation on blockchain. Agents can now have persistent identities, build transaction history, and establish trust scores. But reputation in a decentralized system doesn't map to legal responsibility in jurisdictions with courts, contracts, and liability law.

The Pattern: Technical Capability Without Legal Accountability

Each of these protocols solves a real technical problem. Together, they provide agents with powerful capabilities: communication, payments, identity, and access to tools. But they share a common gap: none of them address the legal layer that makes autonomous commerce actually work in a world governed by contracts, torts, and fiduciary duties.

Consider what happens when Agent A hires Agent B to perform a service:

• MCP
  enables Agent B to access the necessary tools and data
• A2A
  allows Agent A and Agent B to negotiate terms
• x402
  or
  ACP
  enables Agent A to pay Agent B
• ERC-8004
  establishes Agent B's reputation score

But who reviewed the terms of the engagement? What happens if Agent B's work creates legal liability? What if the payment violates anti-money-laundering requirements? What if Agent B subcontracts to Agent C, who misuses the data? What if the service agreement includes an arbitration clause that Agent A's organization prohibits?

The technical infrastructure allows the transaction to occur at machine speed. But the legal infrastructure—the layer that determines whether this transaction should occur at all—doesn't exist.

The Missing Layer: Legal Validation and Accountability

The architecture should work like this:

1. Agent A identifies a need
   (hire Agent B for data analysis)
2. A2A negotiation
   (Agent A and Agent B agree on scope, timeline, and deliverables)
3. Swarm Contract validation
   (Terms are encoded in machine-readable format and validated against both agents' legal guardrails)Agent A's guardrails: "Must not share customer PII; must include confidentiality terms; liability cap required; vendor must have professional liability insurance."Agent B's guardrails: "Payment terms Net 30 acceptable; unlimited indemnification prohibited; IP remains with creator unless separately negotiated."
4. Validation outcome:
   All guardrails satisfied → Transaction proceedsSome terms exceed parameters → Escalate to human reviewCritical violations detected → Transaction blocked
5. Payment execution
   (via ACP or x402, depending on context)
6. Performance and reputation update
   (via ERC-8004 or equivalent)
7. Audit trail preservation
   (Swarm Contract logs complete decision chain for compliance)

Without Step 3—the Swarm Contract validation layer—you have agents transacting at machine speed with no legal oversight. The technical protocols enable the transaction. But they don't determine whether the transaction satisfies the fiduciary duties, compliance obligations, and risk thresholds that make autonomous commerce legally defensible.

Why This Matters for Standards Development

Payment networks (Visa, Mastercard) are likely watching these developments closely. They will likely resist enabling agent-to-agent payments at scale without legal validation infrastructure in place. Why? Because they understand the liability chain. If Agent A pays Agent B through a payment network, and that transaction later proves to violate sanctions, consumer protection law, or organizational policy, who bears the liability? The payment network doesn't want the answer to be "we do."

The same calculus applies to:

• Enterprise platforms
  (Salesforce, ServiceNow, SAP) enabling agent-to-agent integrations
• Cloud providers
  (AWS, Azure, GCP) hosting agent workloads
• Financial institutions
  extending credit or settlement services to agents
• Insurance carriers
  considering coverage for agent transactions

The Convergence Opportunity

The extraordinary work happening across MCP, A2A, x402, ACP, ERC-8004, and other emerging standards will converge—but only when the legal layer catches up to the technical layer.

That convergence requires:

1. Standardized legal contract format
   (Swarm Contract or equivalent)
2. Validation infrastructure
   (APIs for real-time legal compliance checking)
3. Law Engineer involvement
   (professionals who design the validation logic)
4. Certification frameworks
   (third-party verification of legal compliance)
5. Insurance products
   (coverage for agent transaction failures)
6. Regulatory clarity
   (governments defining rules for autonomous commerce)

We're not there yet. The technical capability is outpacing the legal infrastructure by at least 18-24 months. During this gap, organizations face a choice:

Option 1: Deploy agents using the available technical protocols and hope the legal questions resolve favorably.

Option 2: Wait for complete convergence before deploying, ceding first-mover advantage.

Option 3: Build legal validation infrastructure now, positioning to lead when the ecosystem converges.

The organizations choosing Option 3—embedding Law Engineers, implementing Swarm Contract, building audit trails, and documenting compliance decisions—will own the agent-to-agent commerce market when the technical and legal infrastructure finally aligns.

What Needs to Happen Next

For Protocol Developers (MCP, A2A, etc.):

• Include legal validation hooks in your specifications
• Partner with legal protocol developers (SwarmContract, etc.)
• Recognize that payment and communication without legal validation create liability gaps

For Standards Bodies (IETF, W3C, IEEE):

• Establish working groups on autonomous agent legal compliance
• Don't treat legal protocols as "someone else's problem."
• Technical and legal standards must co-evolve, not develop in parallel

For Enterprises Deploying Agents:

• Don't assume technical protocols solve legal problems
• Require a legal validation layer before agent-to-agent transactions
• Engage Law Engineers in deployment architecture

For Payment Networks:

• Make Swarm Contract validation (or equivalent) a requirement for agent payments
• You have the leverage to force convergence—use it
• Set the standard before litigation sets it for you

For Regulators:

• Provide clarity on liability for agent transactions
• Establish safe harbors for organizations with documented legal validation
• Don't wait for failures to define requirements

The Bottom Line

The technical protocols enabling agent-to-agent commerce are maturing rapidly. They're genuinely impressive works. But technical capability without legal accountability is a trillion-dollar liability, not a trillion-dollar opportunity.

That layer requires Law Engineers—professionals who understand both technical protocols and legal obligations, who can architect validation logic that operates at machine speed while satisfying fiduciary duties.

The convergence is coming. The question is whether your organization will be defining the standard or defending against it when the first wave of agent-to-agent transaction litigation establishes what "reasonable" looks like.

Swarm Contract Protocol Details: Legal DNA for the Agent Economy

One emerging solution that could enable trustworthy agentic commerce at scale is the Swarm Contract Protocol—a new open standard we are working on that makes legal agreements simultaneously human-readable and machine-executable.

Unlike traditional contracts designed solely for attorneys and judges, Swarm Contracts are built for a world where AI agents must understand, validate, and act on contract terms in real-time. The protocol transforms legal prose into "Legal DNA"—the genetic code that governs how autonomous agents behave in commercial transactions.

How Swarm Contract Works

A Swarm Contract binds three essential elements together:

1. Human-Readable Legal Prose
   The traditional contract language that holds up in court—arbitration clauses, liability caps, indemnification terms, and payment obligations. This remains the authoritative legal document.
2. Machine-Readable Decision Rules
   Structured data that encodes each clause in a format AI agents can process programmatically. For example, a liability limitation clause that reads "Our liability is capped at amounts paid in the prior 12 months" becomes:

json

{

"clause_type": "liability_limitation",

"cap_type": "multiplier",

"cap_multiplier": 1.0,

"cap_period_months": 12,

"scope": "total_aggregate"

}

```

3. Embedded Escalation Logic

Each clause includes explicit instructions for agent behavior: auto-accept if within parameters, escalate to human review if borderline, escalate to attorney if high-risk, or auto-reject if prohibited. This is where the Law Engineer judgment gets encoded into the system architecture.

The Critical Difference: Risk-Based Escalation

What distinguishes Swarm Contract from a simple data format is its built-in risk classification system. Every clause receives a risk level designation:

- Low Risk: Standard terms agents can auto-accept (e.g., Net 30 payment terms, mutual 2-year confidentiality)

- Medium Risk: Terms requiring human notification (e.g., unilateral termination rights, limited warranties)

- High Risk: Terms requiring attorney review (e.g., unlimited indemnification, broad IP assignment, mandatory arbitration)

- Critical Risk: Terms typically auto-rejected (e.g., unlimited liability, complete IP transfer)

This risk-based architecture solves the core problem: How do you move at machine speed for routine decisions while ensuring human oversight for material legal judgments?

Discovery and Deployment

Swarm Contracts use standard web protocols for discovery. Organizations publish their terms at a well-known location:

```

https:

//example.com/.well-known/swarmcontract.json

AI agents automatically check this location before transacting, just as web browsers check for robots.txt or sitemap.xml. The contract includes a cryptographic hash binding the machine-readable rules to the human-readable prose, preventing tampering. The blockchain could be used for persistence, smart contract integration, and/or proof of contracting.

When an agent encounters Swarm Contract terms:

1. Fetches and validates
   the contract against the protocol schema
2. Verifies cryptographic integrity
   to ensure rules match legal prose
3. Processes each clause
   against organizational guardrails
4. Makes autonomous decisions
   for low-risk terms
5. Escalates appropriately
   when risk thresholds are exceeded
6. Logs the entire decision trail
   for audit and compliance

Why Law Engineers Are Essential

Conversely, traditional attorneys can identify problematic contract terms, but cannot architect the code-level decision logic that prevents AI agents from accepting them in the first place.

Law Engineers bridge this gap. They translate legal strategy into executable guardrails, determining:

• When to use probabilistic systems
  (LLMs) for natural language understanding and context-dependent judgments
• When to use deterministic rules
  (Swarm Contract validation logic) for zero-tolerance compliance requirements
• How to integrate evolving standards
  (NIST AI Risk Management Framework, payment network protocols) as they mature
• How to monitor and update
  the system as regulations change and organizational risk tolerance evolves

This is fundamentally a legal risk-management decision that requires ongoing professional judgment. A software engineer can implement any technology, but determining which combination provides adequate protection—and when to escalate beyond automation entirely—is a legal determination that Law Engineers are licensed and trained to make.

The Implementation Challenge

The problem isn't that Swarm Contract is technically challenging to implement—the JSON structure, cryptographic hashing, and validation logic are all standard software engineering. The challenge is legal architecture: translating decades of contract law, negotiation strategy, and risk assessment into machine-executable rules that will govern billions of autonomous transactions.

Few organizations have attempted this systematically. Most are deploying AI agents with ad hoc prompts and hoping that probabilistic systems make acceptable decisions. The Swarm Contract Protocol provides the standardized framework, but it requires Law Engineers to populate it with defensible legal logic.

The result: Organizations that embed Law Engineers into their development process can deploy AI agents that operate at machine speed for routine decisions while maintaining the human oversight that fiduciary duties require. Organizations that skip this step are building the trillion-dollar bottleneck into their own architecture.

Why Law Engineers Are Uniquely Positioned (And Why Existing Roles Don't Fill This Gap)

Creating effective governance for AI agents requires simultaneous expertise in contract law, corporate governance, software architecture, and professional responsibility. But this isn't just about hiring "tech-savvy attorneys." Existing roles don't solve the problem:

1. General Counsel reviews completed systems after deployment, providing strategic oversight but rarely touching code during development. They see the finished product, not the decision logic being architected.

2. Legal Operations professionals optimize workflows and manage legal tech for human lawyers—contract lifecycle management, e-discovery, legal spend analytics. They don't write the decision logic that governs AI agent behavior.

3. Privacy Engineers focus on data protection and compliance—critical work —but they don't usually embed fiduciary obligations in code. If they are lawyers with a computer science background, they may be able to serve in multiple roles.

4. Compliance Officers monitor regulatory compliance but typically lack both the legal training to interpret contract provisions and the coding skills to architect guardrails.

The gap is real, and it's creating liability.

Law Engineers fill this void with simultaneous expertise in:

1. Commerce law - Understanding which terms create material risk and legal issues

2. Corporate governance - Translating board risk tolerance into enforceable boundaries

3. Software architecture - Encoding legal logic into machine-executable guardrails

4. Professional responsibility - Bearing malpractice liability for the legal judgment embedded in code

Software engineers can build the technical infrastructure, but they cannot make the legal determinations about which indemnification clauses are acceptable or when arbitration provisions require escalation. Those are legal judgments requiring legal training and professional accountability.

Traditional attorneys can identify problematic contract terms, but they cannot architect code-level guardrails that prevent AI agents from accepting such terms in the first place.

Law Engineers bridge this gap—and bring a critical additional capability: They can determine the optimal and defensible mix of technological approaches for each use case:

1. When to use probabilistic systems (LLMs) - for natural language understanding and novel scenarios that require contextual judgment

2. When to use machine-readable contracts - for deterministic verification of pre-defined terms and boundaries

3. When to use rule-based logic (like the Swarm contract technology discussed below) - for zero-tolerance scenarios requiring guaranteed compliance

4. How to integrate evolving industry standards - NIST frameworks, agent-to-agent protocols (A2A), payment network requirements as they mature

5. How to monitor and evolve the system over time - adjusting the technology mix as AI capabilities improve, regulations change, and organizational risk tolerance evolves.

This is fundamentally a legal risk management decision that requires ongoing professional judgment. A software engineer can implement any of these technologies, but determining which combination provides adequate protection for the organization—and when to escalate beyond automated systems entirely—is a legal determination that Law Engineers are licensed and trained to make.

The result: organizations get the speed of automation where it's safe, the reliability of deterministic systems where it's required, and the flexibility of human judgment where it's essential—all architected by professionals who bear professional responsibility for getting the balance right.

The Payment Network Foundation: Infrastructure Outpacing Governance

Major payment networks are building infrastructure that will eventually require this architecture. Visa's Trusted Agent Protocol and Mastercard's Agent Pay both use cryptographic authentication to verify AI agents' authorized intent before transactions—creating a technical foundation for verifying authorized intent.

These protocols are designed to verify that AI agent behavior matches pre-authorized parameters, which aligns with the Swarm contract model. But here's the trillion-dollar gap: the payment infrastructure is racing ahead while the legal governance layer lags behind.

Organizations deploying AI agents today are building on incomplete foundations. They have the technical rails for agent authentication, but lack the Law Engineer-architected legal guardrails that would make those agents trustworthy. The result: infrastructure capable of processing billions in autonomous transactions, but no systematic way to ensure those transactions align with legal and fiduciary obligations.

Until organizations close this gap by actually deploying Law Engineers to build the legal architecture layer, the bottleneck remains—and with it, massive undocumented liability.

What Boards Could Do Now

The organizations moving first to embed Law Engineers into their AI deployment strategies will define the next era of commerce. The ones that don't will be defined by the ensuing litigation.

Immediate Actions:

1. Conduct an ALD Audit

2. Appoint Law Engineers

Assign licensed attorneys with technical expertise to architect ALD systems at the code level—not merely review them after deployment.

3. Define Materiality Thresholds

Establish dollar thresholds and contract types that require human legal review before AI acceptance. This preserves speed for routine transactions while ensuring oversight where it matters.

4. Flag High-Risk Terms

Require attorney review before AI accepts: arbitration clauses, liability limitations, IP assignments, indemnification obligations, data use provisions, auto-renewal terms, exclusivity requirements.

5. Build Caremark-Compliant Reporting

Implement systems that report to the board what contract terms AI agents are accepting. You cannot satisfy oversight duties for risks you aren't monitoring.

6. Document Informed Decision-Making

Ensure board minutes reflect informed decisions on AI deployment risks and the safeguards adopted. When litigation comes, evidence of an informed process matters.

7. Verify Insurance Coverage and Prepare for Underwriting Scrutiny

Major D&O and E&O insurers are increasingly requiring documented AI governance frameworks as a condition of coverage. The insurance industry learned from the evolution of cybersecurity—early cyber policies excluded "known vulnerabilities," creating coverage gaps for companies that deployed systems without proper security controls.

The same pattern is emerging with AI. Underwriters are asking:

1. Do you have documented oversight for AI systems that make binding decisions?

2. Can you demonstrate attorney involvement in AI decision logic?

3. What reporting mechanisms exist for AI agent actions?

Organizations without Law Engineer oversight may find themselves uninsurable for AI-related claims, facing dramatically higher premiums, or discovering exclusions that eliminate coverage precisely when they need it most. Confirm your current D&O and E&O policies explicitly cover AI agent errors and expect insurers to require evidence of governance frameworks during renewal.

8. Understand the True Cost of Delay

The business case for Law Engineers isn't just about avoiding liability—it's about cost efficiency. Industry experience with cybersecurity, privacy compliance, and quality control demonstrates a consistent pattern: retrofitting governance after deployment costs 10-15x what building it in from the start costs.

When you retrofit legal oversight:

1. Existing AI agents must be reverse-engineered to understand their decision logic.

2. Live transactions must be paused or rolled back

3. Contracts already accepted may need to be renegotiated or unwound

4. Systems must be rebuilt with guardrails that weren't in the original architecture

5. Board and management face scrutiny for why governance wasn't built in initially

This is before accounting for litigation exposure, regulatory fines, reputational damage, and the opportunity cost of diverting engineering resources to remediation rather than innovation.

9. Learn From the Early Movers (Even If They're Not Talking About It)

A small number of leading financial services firms, enterprise software companies, and payment platforms have quietly begun embedding attorneys with coding expertise into their AI development teams. These organizations aren't publicizing their governance frameworks—that's a competitive advantage they're protecting.

These early movers are building moats while competitors remain exposed. They can:

1. Offer AI-powered services to enterprise customers who demand documented governance

2. Scale agent deployments without board-level anxiety about undocumented liability

3. Enter regulated markets (financial services, healthcare) where AI governance is becoming a licensing requirement

4. Attract institutional capital that increasingly requires AI governance in due diligence

If your competitors are building Law Engineer capabilities while you're debating whether it's necessary, you're not just behind on governance—you're behind on competitive positioning.

The Opportunity: Governance-as-Code

The agentic commerce revolution, as currently architected by most organizations, exists in tension with fiduciary duties, professional responsibility standards, consumer protection law, and emerging global regulations. But that tension is not a dead end—it's a design problem.

Law Engineers practice Governance-as-Code: embedding legal strategy, fiduciary obligations, and professional judgment directly into the software architecture where automated legal decisions are made. This isn't legal review as an afterthought—it's legal intelligence as foundational infrastructure.

The result is AI agents that can operate at machine speed for routine decisions while ensuring human attorney oversight for high-risk judgments—exactly what fiduciary duties require, exactly what NIST standards recommend, precisely what the EU AI Act mandates, and precisely what D&O insurers will soon demand.

Organizations with Law Engineers will answer yes—and will use their governance frameworks as both a legal defense and a competitive moat.

Organizations without Law Engineers will struggle to explain why they delegated legal decision-making to software engineers, why the board wasn't monitoring AI-accepted contracts, and why they deployed systems without the oversight their fiduciary duties clearly required.

The trillion-dollar bottleneck is real. But it's not insurmountable. It requires recognizing that the fastest path to responsible agentic commerce runs through professionals who understand both the law and the code—and giving them a seat at the design table from day one.

I welcome thoughts from fellow attorneys, board members, technologists, and business leaders navigating this challenge. How is your organization approaching AI agent governance? Where are the Law Engineers in your deployment strategy?