Who carries responsibility when an AI agent begins reviewing contracts, applying regulatory rules or making commercial decisions on behalf of an organization? In this episode of Tech Talks Daily, I speak with John Nay, founder and CEO of Norm Ai, about Agentic Law and the attempt to redesign legal work around AI agents, legal engineers and experienced attorneys. John has worked across AI, law and public policy for approximately 14 years. His early research adapted neural network methods to legal and government text before large language models became a commercial force. The company information supplied for this episode states that Norm Ai recently raised $120 million in Series C funding at a $1.2 billion valuation, bringing total funding to over $260 million. Norm also says organizations representing over $30 trillion in assets under management use its technology for legal and compliance work. Those figures provide useful context for the scale of interest, while our conversation concentrates on how the model works and where responsibility remains human. John describes Norm Ai as automating the first pass of legal and compliance tasks. One example involves an in house team using an agent to review communications against relevant rules before a person finalizes the decision. Another involves Norm Law receiving transaction documents, assigning the first analysis to AI agents and then presenting the output to an experienced attorney. The attorney decides what happens next, communicates with the client and supervises anything leaving the firm. That division of labor matters because legal reasoning contains several layers. Some work can be handled through deterministic rules. Frontier models can then apply guidance and precedent to new situations. Human judgment remains responsible for high stakes advice and the review of agent output. John also stresses that the model is not making an isolated request to a generic system. Legal judgment is embedded in the way agents are designed, tested and called before live matters enter the workflow. We discuss legal engineering as the bridge between software and professional practice. Norm's legal engineers are trained attorneys who spend much of their time building, testing and validating agents. They work with practicing lawyers, clients and AI engineers to translate preferences, policies and matter specific requirements into systems that can operate within real workflows. Pricing is another part of the model. Norm Law prices selected matters around outcomes rather than hours. John acknowledges the limits. Some complex work can be scoped with enough confidence for a fixed price, while highly unpredictable litigation is much harder to price upfront. The opportunity is to give AI the incentive to examine additional documents and identify inconsistencies without increasing a client's bill for every human hour. The conversation then moves to the proposed Delaware AI Company initiative. John describes it as a regulatory sandbox for a legal entity managed by an AI agent while humans remain involved in its creation and supervision. His argument is that autonomous agents will take increasingly consequential economic actions, so policymakers must decide whether those activities happen within a recognized legal order or outside it. The proposal is designed to test questions around liability, disclosure, capitalization and government oversight before any broader model is adopted. John also believes companies deploying agents today should consider supervisory AI. If an operational agent can act faster and at greater volume than a person, a human team may be unable to inspect every decision. A second agent can evaluate the first against laws, regulations and company policies, with people retaining authority over exceptions and consequential outcomes. Does adding a supervisory agent create stronger accountability, or does it introduce another system whose reasoning must also be tested and questioned? Listen to the episode and share your thoughts with me.

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