Over the past decade, AI has made dramatic progress in understanding and generating language. This leap came from a new model architecture called the Transformer, which was developed to solve problems associated with sequence transduction, or neural machine translation.
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The Pros and Cons of AI in Law
Over the past decade, AI has made dramatic progress in understanding and generating language. This leap came from a new model architecture called the Transformer, which was developed to solve problems associated with sequence transduction, or neural machine translation.
This model can solve any task that changes an input order to an output sequence. It enables AI systems to pay attention to relationships across entire documents instead of processing text one word at a time. This breakthrough led to the development of large language models (LLMs) like GPT and Gemini.
In the legal industry, these LLMs are used to summarize contracts, answer law-related questions, and draft clauses that sound like they were written by a trained lawyer.
The results have been impressive. OpenAI’s GPT-4 famously passed a simulated bar exam with a score in the top tier of test takers. This felt like proof to many observers that AI was ready for real legal work.
However, exams are controlled environments. In the real world, legal practice is uncontrolled and subject to uncertainty, with small mistakes possibly leading to serious consequences.
Moreover, many general-purpose AI systems still struggle with several problems. First, they “hallucinate.” When they do not know an answer, they often guess or make up information instead of saying so.
These systems also do not truly reason about legal rules and consequences. They recognize patterns, but do not understand why a rule exists or when an exception applies.
Lastly, many systems tend to have jurisdictional blind spots. A clause that works in New York may be invalid in California or Switzerland, but a generic AI model may not notice the difference.
A useful analogy here is having a very confident intern in a law firm. They write quickly, sound professional, and rarely hesitate, but they still need supervision.
Unfortunately, unsupervised confidence is a liability in law.
Solving Legal AI’s Trust Problem
To make AI reliable enough for legal work, the industry is moving beyond single, standalone models toward agentic legal systems.
Agentic models are AI systems consisting of AI agents that do more than predict text. They can plan tasks, use tools, check their own work, and correct mistakes. Instead of acting like talking encyclopedias, they behave more like a junior legal team that follows a process.
Their benefits and features include:
Multi-agent architecture
Agentic systems like Laine AI that are designed for the legal industry rely on various agents to do different tasks. For instance, one agent drafts a clause, while another checks it for legal logic. At the same time, a third looks up the relevant law, and a coordinating agent manages the workflow and decides what happens next.
This operation mirrors how real law firms operate. Junior lawyers draft, while senior lawyers review and specialists research edge cases.
For example, if a drafting agent writes a termination clause, a validation agent can immediately check whether required notice periods are included. If something is missing, the system can fix it before the document ever reaches a human.
By dividing responsibilities, the system reduces single-point failures and makes errors easier to catch.
Knowledge grounding
One of the biggest risks with general AI is that it relies on memory rather than rules. Agentic systems solve this by grounding generation in a structured knowledge base.
The Laine system uses a “Methodology Lake,” which is a database of domain-specific guidance encoded in a machine-readable format. It acts like a living rulebook or playbook that defines what clauses are required for each contract type, how sections relate to one another, and which rules apply in different jurisdictions.
Instead of asking the AI to “figure it out,” the system ensures the necessary elements are included and present in a particular order.
The AI fills in the language, but the structure comes from encoded legal methodology.
Jurisdictional compliance agents
Laws differ from place to place. A contract that is legal in one jurisdiction may be invalid in another.
Agentic systems address this with jurisdiction-specific agents that act like regional legal experts.
For instance, with Laine AI, if a contract is governed by California law, a California compliance agent is activated. This agent checks for mandatory clauses, prohibited language, and recent regulatory changes.
If the governing law is Swiss, a different agent applies different rules.
This type of AI for lawyers eliminates the “one-size-fits-all” problem that plagues generic legal AI. It also allows systems to be updated quickly when laws change without the need to retrain the entire model.
Human-in-the-loop validation
Agentic legal platforms are designed to include humans at critical points. For high-stakes documents, the system routes drafts to independent lawyers for review.
Laine’s AI highlights areas of uncertainty and shows the sources it used. This makes it easy for a lawyer to approve or edit the result. Over time, the collected feedback helps improve the system’s rules and checks, without copying individual lawyers’ styles or exposing client data.
The goal is not to replace lawyers, but to let AI handle the mechanical work.
Why This Matters Now
Agentic systems offer several clear advantages. For one, tasks that once took days can now be completed in minutes. The outputs also come with fewer mechanical errors and documentation of how decisions were made. Users can easily trace each clause back to a rule, a source, or a specific agent’s check.
This level of traceability is crucial in regulated industries. Courts, regulators, and clients want to know not just what an AI produced, but why.
The implications extend beyond the law. Healthcare, finance, and scientific research face similar challenges, including strict rules and low tolerance for error. Agentic systems offer a way to overcome these hurdles by combining learning with structure and oversight.
Multi-party collaboration is also a possibility. Instead of helping just one side draft a document, future systems may support negotiations among multiple parties, allowing them to track positions, highlight trade-offs, and reduce friction.
This would move AI from a drafting assistant to a collaboration infrastructure.
Attention Was Necessary, But Not Sufficient
The Transformer revolution gave AI the ability to understand and generate language at unprecedented levels. That was a necessary breakthrough. Without it, legal AI would not exist in any meaningful form.
But fluency alone does not create trust. High-stakes work demands systems that can check themselves, follow rules, adapt to context, and involve humans when it matters. Agentic legal systems represent this next phase of AI maturity. They combine attention-based models with tools, structured knowledge, multiple agents, and human oversight.
The lesson is broader than law. As AI moves deeper into real-world decision-making, success will come from combining different paradigms into systems that are not just impressive but trustworthy.
