Beyond "Stochastic Parrots": Towards Agentic AI for Legal Reasoning

Abstract: This article argues that current large language models (LLMs), while impressive in their ability to generate text, are fundamentally limited in their capacity for legal reasoning. We contend that the prevailing paradigm of scaling up existing models is insufficient to achieve true legal AI. Instead, a shift towards agentic AI—systems capable of proactive analysis, goal-oriented reasoning, and structured legal knowledge—is required. Through a dialogue between a lawyer and an AI, we explore the shortcomings of current LLMs, outline the principles of a new architectural approach, and present the Legalito platform as a step towards practical, agentic AI in law. We conclude with a call for further research and collaboration in this critical area. 1. Introduction: The Allure and Illusion of AI in Law • AI is rapidly transforming many industries, and law is no exception. • LLMs offer the promise of automating tasks, improving efficiency, and increasing access to legal information. • However, the hype surrounding AI often outpaces its actual capabilities, particularly in areas requiring complex reasoning, such as legal practice. • This article challenges the assumption that "bigger is better" in AI, arguing that a fundamental shift in approach is needed to create AI systems that can truly think like lawyers. • We draw on a dialogue between a lawyer developing real-world AI legal tools (including the Legalito.ar platform) and an advanced LLM (Gemini 2.0 Pro Experimental 02-05) to illustrate the limitations of current technology and to explore a more promising path forward. This article, based on a dialogue between a lawyer and an AI, uses the complexities of legal reasoning – specifically, the concept of stability in Argentine public employment – as a case study to explore the broader limitations of current large language models (LLMs) and to propose a new approach to AI development. However, the challenges and principles we discuss are not unique to law. They are relevant to any field that requires deep understanding, contextual reasoning, and the ability to construct and evaluate complex arguments. While our examples draw from the legal domain, the insights gained apply equally to fields such as medicine, engineering, scientific research, policy analysis, and many others. The need for AI systems that can go beyond "stochastic parrots" and truly reason is a universal one. 2. The "Stochastic Parrot" Problem: Why LLMs Struggle with Legal Reasoning • LLMs are trained to predict the next word in a sequence, based on massive amounts of text data. They excel at mimicking human language, but lack genuine understanding. Large Language Models (LLMs) like Gemini, GPT-4, and others have taken the world by storm. Their ability to generate human-quality text, translate languages, write different kinds of creative content, and answer your questions in an informative way is undeniably impressive. You can ask them to write a poem, summarize a complex article, or even generate code, and they will often produce surprisingly good results. But beneath the surface of this impressive performance lies a fundamental limitation: LLMs, at their core, are prediction machines, not reasoning engines. They are incredibly sophisticated "stochastic parrots," as some researchers have called them – able to mimic human language with remarkable fluency, but lacking genuine understanding of the meaning and implications of the words they are using. What does it mean to be a "stochastic parrot"? Imagine a parrot that has been trained to repeat phrases it has heard from humans. The parrot might be able to say "Polly wants a cracker!" or even "Two plus two equals four," but it doesn't understand the concepts of hunger, crackers, addition, or equality. It's simply repeating patterns it has learned, without any comprehension of their meaning. LLMs are similar, but on a vastly larger and more complex scale. They are trained on massive amounts of text data – books, articles, websites, code, and more. This data is used to build a statistical model of language. This model essentially captures the probabilities of different words (or, more precisely, "tokens," which can be words, parts of words, or punctuation marks) appearing in different contexts. When you give an LLM a "prompt" (a piece of text), it uses this statistical model to predict the most likely sequence of words that should follow the prompt. It's like a super-powered autocomplete, drawing on its vast "memory" of text patterns to generate a response that is statistically likely to be relevant and coherent. The Problem with Prediction: This "predict the next word" approach works surprisingly well for many tasks. LLMs can generate text that is grammatically correct, stylistically appropriate, and often factually accurate. But it's fundamentally different from human reasoning, which involves: • Understanding Concepts: Knowing what words mean, not just how they are used. • Making Inferences: Drawing logical conclusions from information, even if it's not explicitly stated. • Applying Rules: Using general rules (like laws) to specific situations. • Reasoning about Cause and Effect: Understanding how actions lead to consequences. • Contextual Awareness: Taking into account the broader context (social, historical, legal) when interpreting information. • Goal-oriented action: Having a final goal, and evaluating the actions that achieve it. LLMs, in their current form, struggle with all of these aspects of reasoning. They can mimic reasoning, by generating text that sounds like a logical argument, but they are not actually reasoning. They are simply predicting the next word, based on statistical patterns. Why This Matters for Law: The limitations of LLMs are particularly problematic in the legal domain, where precise reasoning, contextual understanding, and logical consistency are crucial. A lawyer doesn't just string together legal-sounding words; they: • Analyze the facts of a case. • Identify the relevant legal rules and precedents. • Apply those rules and precedents to the facts. • Construct a logical argument to support their client's position. • Anticipate and refute counterarguments. • Adapt their arguments to the specific circumstances of the case. An LLM might be able to generate a legal brief that looks convincing on the surface, but it might contain subtle errors, inconsistencies, or misinterpretations of the law that a human lawyer would easily spot. It might miss crucial nuances, fail to consider relevant precedents, or make illogical arguments. The Stability of Public Employment Example The stability of public employment is not a mere "string of words". It implies a concept, in which the courts (and specially, the Supreme Court of Argentina) have been clear about. The courts recognized the stability of the public employee, when he has been transfered from the provincial goverment, to a society of the national goverment. This particular case, shows the limitations of the current LLMs, because it´s not only about statistics, it requires a complex understanding of the law, and how a principle is applied, even when the employer has changed. In short: LLMs are powerful tools for generating text, but they are not yet capable of the kind of deep understanding and reliable reasoning that is required for legal practice. They are "stochastic parrots," not legal eagles. And that's why we need a new approach to building AI systems for law. Examples: Where LLMs Fall Short To illustrate the limitations of LLMs in legal reasoning, let's consider some examples, ranging from simple to complex: Basic Examples: 1. The Ambiguous Contract Clause: o Scenario: A contract clause states: "The seller shall deliver the goods to the buyer's place of business." The buyer has two places of business: a warehouse and a retail store. The goods are perishable and should be delivered to the warehouse, but the LLM, trained on general text, might not know this. o LLM (Potential Response): "The goods should be delivered to the buyer's place of business." (Repeats the clause without resolving the ambiguity). o Lawyer (Reasoning): A lawyer would consider:  The nature of the goods (perishable).  The purpose of the contract (likely to preserve the goods).  Industry practice (where are such goods typically delivered?).  Prior dealings between the parties.  Any other relevant clauses in the contract.  The context o Why the LLM Fails: The LLM lacks common-sense knowledge about perishable goods and business practices. It also lacks the ability to reason about the purpose of the contract and to integrate information from multiple sources. 2. The "Open Texture" of Legal Terms: o Scenario: A law prohibits "vehicles" in a park. A child is riding a scooter in the park. Is a scooter a "vehicle" under the law? o LLM (Potential Response): "A vehicle is a means of transport. A scooter can be a means of transport. Therefore, a scooter is a vehicle." (Applies a simple definition, but misses the nuance). o Lawyer (Reasoning): A lawyer would consider:  The purpose of the law (likely to prevent noise, pollution, or danger to pedestrians).  The legislative history of the law (what did the lawmakers intend to prohibit?).  Prior court decisions on similar cases.  The social context (are scooters commonly used in parks? Are they considered dangerous?). o Why the LLM Fails: The LLM applies a literal definition without considering the purpose of the law or the context. Legal terms often have "open texture," meaning their meaning is not fixed but depends on the context. Intermediate Examples: 1. The Misleading Precedent: o Scenario: A lawyer finds a case that seems to support their client's position, but the case is distinguishable (different in a legally significant way) from the current case. o LLM (Potential Response): "The case of Smith v. Jones supports the argument that..." (Cites the case without analyzing its relevance). o Lawyer (Reasoning): A lawyer would carefully analyze the facts and the legal reasoning of the prior case to determine if it is truly applicable to the current case. They would look for distinguishing factors. o Why the LLM Fails: The LLM can find cases that mention similar terms or concepts, but it may not be able to assess the relevance of those cases in a nuanced way. It lacks the ability to reason by analogy and to distinguish cases based on subtle differences. 2. The Contradictory Evidence: o Scenario: In a contract dispute, there is conflicting evidence about the terms of the agreement. One party claims there was an oral agreement, the other denies it. There are emails and documents that partially support each side. o LLM (Potential Response): The LLM might summarize the evidence on both sides, but it might not be able to weigh the evidence and reach a conclusion about which side is more credible. o Lawyer (Reasoning): A lawyer would assess the credibility of the witnesses, analyze the consistency of the evidence, and apply legal rules about the admissibility and weight of evidence. o Why the LLM Fails: The LLM lacks the ability to make judgments about credibility and to resolve conflicts in evidence. It can present the information, but it can't evaluate it in a legally meaningful way. Advanced Examples: 1. The Novel Legal Issue: o Scenario: A case presents a new legal issue that has never been decided by the courts before. There is no directly applicable statute or precedent. o LLM (Potential Response): The LLM might be able to find cases that are somewhat related, but it won't be able to construct a novel legal argument based on general principles of law. o Lawyer (Reasoning): A lawyer would:  Analyze the underlying principles of the relevant areas of law.  Reason by analogy from existing cases.  Consider the policy implications of different legal rules.  Construct a creative argument based on general principles and public policy. o Why the LLM Fails: The LLM is limited by its training data. It can't reason about situations that are fundamentally new. It lacks the ability to extrapolate from existing knowledge to create new legal arguments. 2. The Ethical Dilemma: o Scenario: A lawyer faces an ethical dilemma. They have a duty to their client, but also a duty to the court and to the legal system. These duties may conflict. o LLM (Potential Response): The LLM might be able to identify the relevant ethical rules, but it won't be able to weigh the competing duties and make a judgment about what to do in the specific situation. o Lawyer (Reasoning): A lawyer would: 1. Consider the specific facts of the case. 2. Analyze the relevant ethical rules and precedents. 3. Consult with other lawyers (if necessary). 4. Make a judgment based on their professional experience and ethical conscience. o Why the LLM Fails: The LLM lacks the judgment, experience, and ethical understanding to resolve complex ethical dilemmas. It can identify the rules, but it can't apply them in a nuanced and context-sensitive way. Conclusion (of this section): These examples illustrate that while LLMs can be helpful tools for legal research and writing, they are not yet capable of the kind of deep understanding, contextual reasoning, and ethical judgment that are required for legal practice. They can process information, but they can't think like lawyers – yet. This underscores the need for a new approach to AI in law, one that goes beyond "stochastic parrots" and builds systems that can truly reason. • Legal reasoning requires more than pattern recognition: o Logical Deduction and Induction: Applying general legal rules to specific factual situations. o Contextual Understanding: Interpreting legal concepts in light of their social, political, and historical context. o Dealing with Ambiguity: Resolving conflicting interpretations of laws and precedents. o Constructing Arguments: Building persuasive arguments based on evidence, logic, and legal principles. o Anticipating Counterarguments: Identifying and refuting opposing viewpoints. While LLMs excel at identifying patterns in vast datasets of text, true legal reasoning transcends mere pattern recognition. It's not enough to identify correlations between words or phrases; a lawyer must understand the underlying legal principles, apply them to specific factual situations, and construct logical arguments to reach a desired outcome. This requires a combination of skills that current LLMs, in their "stochastic parrot" form, struggle to replicate. 1. Logical Deduction and Induction: Legal reasoning often involves both deduction (applying general rules to specific cases) and induction (drawing general principles from specific cases). • Deduction Example: o General Rule: "All contracts require offer, acceptance, and consideration." o Specific Case: "Did Party A and Party B have a contract?" o Deductive Reasoning: The lawyer must determine if the facts of the case satisfy the elements of offer, acceptance, and consideration. • Induction Example: o Specific Cases: A series of court decisions where similar fact patterns led to similar outcomes. o Inductive Reasoning: The lawyer might infer a general rule or principle from these cases, even if that rule is not explicitly stated in any statute or precedent. • LLM Limitation: LLMs can identify patterns that resemble deductive or inductive reasoning, but they don't actually reason in this way. They might be able to predict that a certain legal conclusion is likely based on similar cases, but they don't understand why. 2. Contextual Understanding: Legal rules are not applied in a vacuum. They must be interpreted in light of their: • Social Context: The prevailing social norms and values. • Historical Context: The circumstances that led to the creation of the rule. • Economic Context: The economic realities that the rule is intended to address. • Political Context: The political forces that shaped the rule. • Example: A law prohibiting "discrimination" might be interpreted differently in different historical periods, or in different countries with different social norms. • LLM Limitation: LLMs lack real-world knowledge and historical understanding. They can process text that describes these contexts, but they don't understand them in the way a human lawyer does. 3. Dealing with Ambiguity: Legal language is often ambiguous or vague. Words and phrases can have multiple meanings, and legal rules can be open to different interpretations. • Example: The word "reasonable" appears frequently in legal texts ("reasonable care," "reasonable doubt," "reasonable person"). What is "reasonable" in one context may not be "reasonable" in another. • LLM Limitation: LLMs struggle with ambiguity. They tend to favor the most common or statistically likely interpretation, which may not be the correct interpretation in a specific legal context. 4. Constructing Arguments: A lawyer doesn't just apply the law; they construct arguments to persuade a judge or jury that their interpretation of the law is the correct one. • Example: In a contract dispute, a lawyer might argue that: o The contract is valid and enforceable. o The other party breached the contract. o Their client is entitled to damages. Each of these arguments requires supporting evidence, legal precedent, and logical reasoning. • LLM Limitation: LLMs can generate text that resembles a legal argument, but they may not be able to construct a truly persuasive argument that is tailored to the specific facts and legal issues of the case. 5. Anticipating Counterarguments: A good lawyer anticipates the arguments that the opposing party is likely to make and prepares to refute them. • Example: If a lawyer is arguing that a contract is valid, they will also consider possible arguments that the contract is invalid (ej: lack of capacity, fraud, duress) and prepare responses to those arguments. • LLM Limitation: LLMs can generate text that presents different sides of an argument, but they may not be able to anticipate and refute counterarguments in a strategic and effective way. They lack the adversarial thinking that is essential to legal practice. 6. Goal-Oriented Action: • Example: A lawyer, when analizing a case, have a specific goal, to win. A laywer doesn´t pick words at random, they follow an strategy to achieve this goal. • LLM limitation: LLM lack the hability to understand the general picture, in a broad sense. It has been trained to follow instructions, and those instructions can be tricky. The lack of a "goal oriented action" can be traced to the prompt engineering necesary to obtain a result. Conclusion (of this section): Legal reasoning is a complex cognitive process that involves much more than pattern recognition. It requires understanding, judgment, creativity, and strategic thinking. Current LLMs, while impressive in their ability to mimic human language, fall short of these capabilities. To build AI systems that can truly assist lawyers, we must move beyond the "stochastic parrot" paradigm and embrace a new approach that focuses on genuine legal reasoning. • The stability of public employment in Argentina, a recurring theme in our dialogue, serves as a case study illustrating these challenges. 3. The Stability of Public Employment in Argentina: A Case Study in Legal Complexity The principle of "stability" in public employment, as enshrined in Article 14 bis of the Argentine Constitution and further developed in provincial legislation and jurisprudence, provides a compelling case study of the challenges LLMs face in legal reasoning. This seemingly straightforward concept – that public employees should not be dismissed without just cause and due process – reveals, upon closer examination, a level of nuance and contextual understanding that currently eludes even the most advanced language models. 3.1 Initial Understanding (The LLM's "First Attempt"): Initially, the AI, when presented with the concept of "stability" in the context of our hypothetical case (the dismissal of a public employee transferred from a provincial entity to a national one), approached it as a textual pattern. It could identify the relevant legal provisions (Article 14 bis, relevant provincial laws) and generate text summarizing their content. It might even find court decisions mentioning "stability" in public employment. However, as our dialogue progressed, it became clear that this superficial understanding was insufficient. The AI's initial responses, while grammatically correct and seemingly relevant, lacked the depth and nuance required for a legally sound analysis. 3.2 The Iterative Process: Unveiling the Nuances Through a series of questions, prompts, and counter-examples, the lawyer guided the AI towards a more sophisticated understanding of "stability." This process mirrored the way a human lawyer develops expertise – not through rote memorization, but through iterative learning, critical analysis, and engagement with real-world legal problems. Key Insights from Our Dialogue: • "Stability" is Not Absolute: The AI initially treated "stability" as a binary concept – either the employee had it or they didn't. Through our discussion, it became clear that "stability" is a matter of degree and is subject to interpretation and limitations. • The Importance of Context: The AI learned that the meaning and application of "stability" depend on the specific context: o The origin of the employment relationship (provincial vs. national). o The terms of any transfer agreements between entities. o The nature of the employee's duties. o The existence of any prior disciplinary proceedings. • The Role of Jurisprudence: The AI was initially able to find relevant court decisions (e.g., "Madorrán"), but it struggled to apply the principles from those cases to the specific facts of our hypothetical case. Through our dialogue, it learned to distinguish cases, identify relevant ratio decidendi (the reasoning behind the decision), and reason by analogy. • The "Stochastic Parrot" Limitation: The AI, like other LLMs, could generate text that sounded like a legal argument about stability, but it often missed crucial nuances or made logical leaps that a human lawyer would not. This highlighted the difference between pattern recognition and genuine legal reasoning. 3.3 Examples: The specific case brought to the AI, can be described as follows: • A public employee, that gained stability working on a provincial entity. • The employee was transfered to a national entity. • The employee was fired, without a just cause, nor a due process. One first approach, was to consider the LCT (Ley de Contrato de Trabajo, or Work Contract Law) as the main legal framework. But, as it was pointed by the laywer, this framework didn´t apply, because the employee had stability. As the AI, the first answers were focused on the LCT. But as our conversation went on, the AI learned that the stability gained in the provincial administration, could not be ignored. 3.4 Beyond Textual Patterns: Towards True Understanding The case of public employment stability in Argentina demonstrates that legal reasoning requires more than just identifying and processing textual patterns. It requires: • Conceptual Understanding: Grasping the underlying principles and policy goals of legal rules. • Contextual Awareness: Recognizing how those principles apply in different factual situations. • Critical Analysis: Evaluating the strength and weakness of different arguments. • Judgment: Making informed decisions based on a holistic understanding of the law and the facts. Current LLMs, while powerful tools for text processing, fall short of these capabilities. They can mimic legal reasoning, but they don't truly understand it. This is why a new approach to AI in law is needed – an approach that prioritizes genuine understanding and reliable reasoning over mere textual fluency. 4. Next Steps (Moving Forward): In the following sections, we will explore the principles of this new approach, outlining the key features of an "agentic AI" that could truly reason like a lawyer, and discuss how such a system could be developed. We will also reflect on the ethical implications and the future of legal practice in the age of increasingly sophisticated AI. 5. Current Benchmarks: Measuring the Wrong Thing? Existing benchmarks for evaluating LLMs often focus on tasks like text generation, question answering, and translation. While these skills are relevant to legal work, they fail to capture the essence of legal reasoning, which involves deep understanding, contextual analysis, and critical judgment. The progress of AI is often measured by its performance on benchmarks. These are standardized datasets and tasks designed to evaluate specific capabilities, such as: • Text Generation: Can the AI generate text that is grammatically correct, coherent, and stylistically appropriate? • Question Answering: Can the AI answer questions based on a given text or knowledge base? • Translation: Can the AI accurately translate text from one language to another? • Summarization: Can the AI generate concise and accurate summaries of longer texts? LLMs have made impressive strides on many of these benchmarks. They can often produce text that is indistinguishable from human-written text, answer questions with surprising accuracy, and translate languages with remarkable fluency. However, these benchmarks, while useful for evaluating certain aspects of language processing, are fundamentally inadequate for assessing the legal reasoning capabilities of AI systems. They focus on surface-level skills, not on the deep understanding and critical judgment that are essential for legal practice. Why Current Benchmarks Fall Short: 1. Emphasis on Pattern Recognition: Most benchmarks reward LLMs for identifying patterns in the training data and replicating those patterns in their output. This is not the same as understanding the meaning of the text or reasoning about it logically. 2. Lack of Contextual Understanding: Benchmarks often present tasks in a decontextualized way. The AI is given a short text and asked to answer a question, generate a summary, or translate a sentence. But real legal reasoning always takes place in a rich context of facts, laws, precedents, and social norms. 3. No Evaluation of "Reasoning" as Such: Benchmarks typically evaluate the output of the AI (the text it generates), not the process by which it arrived at that output. They don't assess whether the AI used logical reasoning, deduction, induction, or critical analysis. They only assess whether the answer is correct (according to some predefined criteria). 4. Focus on Closed-Ended Questions: Many benchmarks rely on closed-ended questions (multiple choice, true/false) that can be answered by pattern matching without genuine understanding. Legal reasoning, however, often involves open-ended questions that require complex analysis and judgment. 5. "Gaming the System": LLMs can be trained to "game the system" – to achieve high scores on benchmarks by exploiting statistical biases in the data, without actually understanding the underlying concepts. Examples: • Question Answering: An LLM might be able to answer a question about a contract clause by finding a similar clause in its training data and copying the answer. But it might not be able to apply that clause to a novel factual situation or to resolve an ambiguity in the clause. • Summarization: An LLM might be able to generate a grammatically correct summary of a court decision, but it might miss the key legal issue or misinterpret the court's reasoning. • Text Generation: An LLM might be able to generate a legal brief that looks impressive on the surface, but it might contain logical fallacies, inconsistencies, or misstatements of law. The Need for New Benchmarks: "As we discussed early in our conversation, the current metrics are insufficient. I was capable of generating answers related to the Argentinian Work Contract Law, but it wasn´t until you pointed the stability of public employees, that I could understand that the real case was not about it" To truly evaluate the legal reasoning capabilities of AI systems, we need new benchmarks that: • Focus on deep understanding, not just surface-level skills. • Require contextual reasoning and integration of information from multiple sources. • Evaluate the process of reasoning, not just the output. • Include open-ended questions and complex tasks that require judgment and creativity. • Are resistant to "gaming" and statistical biases. Conclusion (of this section): Current benchmarks for evaluating LLMs are not adequate for assessing their ability to perform legal reasoning. They measure the wrong things. To develop AI systems that can truly assist lawyers, we need to rethink how we evaluate AI progress and focus on the core capabilities that define legal expertise. This requires a shift from pattern recognition to genuine understanding and reliable reasoning. Only with more appropiate ways to evaluate LLM´s hability to reason, will we be able to measure real progress. 6. Escaping the "More of the Same" Trap: A Call for Architectural Innovation • The dominant trend in AI research is to scale up existing LLM architectures (more parameters, more data). • This approach is akin to Watzlawick's "attempted solution" that becomes the problem: more of the same will not lead to qualitatively different results. • The OpenAI model 4.5 example shows the limitations of this scaling approach. • We need to move beyond the "stochastic parrot" paradigm and embrace a new vision of AI: agentic AI. The current trajectory of AI development, particularly in the realm of LLMs, is characterized by a relentless pursuit of "more": more data, more parameters, more computing power. While this approach has yielded impressive results in tasks like text generation and translation, it has also become increasingly clear that "more of the same" will not lead to genuine legal reasoning. We are, to borrow a phrase from communication theorist Paul Watzlawick, trapped in a cycle of "attempted solutions" that exacerbate the very problem they are meant to solve. 6.1. Watzlawick and the "Attempted Solution": Paul Watzlawick, a prominent figure in the field of family therapy and communication theory, developed insightful ideas on how humans create and perpetuate their own problems. Change: Principles of Problem Formation and Problem Resolution (1974), co-authored with John Weakland and Richard Fisch, is one of the key texts that inform our analysis. One of his key concepts, relevant to the field of AI, is the "attempted solution" In essence: • A difficulty arises in a system (a family, an organization, an individual's life). • A "solution" is attempted, often based on common sense, past experience, or prevailing beliefs. • The "solution" fails to resolve the difficulty, or even makes it worse. • Instead of questioning the "solution" itself, the system doubles down on it, applying more of the same, believing that the problem is simply a lack of sufficient effort or correct application. • This creates a vicious cycle, where the "solution" becomes an integral part of the problem. 6.2. The LLM Trap: More Data, More Parameters, Less Understanding: The current development of LLMs mirrors this pattern. The "problem" is that LLMs, despite their fluency, lack genuine understanding and reliable reasoning. The "attempted solution" has been to make them larger and train them on more data. • Result: LLMs become better at mimicking human language, but the fundamental limitations remain. They still struggle with: o Logical reasoning. o Contextual understanding. o Dealing with ambiguity. o Constructing complex arguments. o Adapting to novel situations. • The Vicious Cycle: Instead of questioning the underlying architecture of LLMs, the field has largely focused on scaling up existing models, hoping that "more" will eventually lead to "better". 6.3. Our Iterative Journey: Recognizing the Limits: This article itself, and the dialogue that underlies, represents an attempt to break free from the "more of the same" trap. As we have discussed, The AI, initially approached legal reasoning as a text generation task. It could produce grammatically correct and seemingly relevant text, but it often missed crucial nuances, made logical errors, and struggled to apply legal principles to specific factual situations. Through iterative questioning, challenging assumptions, and exploring analogies (such as the concept of "coding" in natural language), both the lawyer (human) and the AI (machine) came to a deeper understanding of the limitations of current LLMs. We realized that: • "Memorizing" is not "Understanding": LLMs are like students who memorize vast amounts of information but cannot apply it creatively or critically. • "Predicting" is not "Reasoning": LLMs predict the next word in a sequence, but they don't engage in the kind of logical, deductive, and inductive reasoning that characterizes legal thinking. • "Fluency" is not "Comprehension": LLMs can generate text that sounds like a legal argument, but they don't understand the meaning or implications of their words. The constant corrections, and the lawyer´s explanation, helped to improve the AI´s reasoning capabilities, but even with those corrections, a need for a change in the focus, was very clear. 6.4. A Call for Architectural Innovation: Escaping the "more of the same" trap requires a fundamental shift in how we approach AI development for legal reasoning. We need to move beyond the paradigm of "bigger is better" and embrace architectural innovation. This means exploring alternative architectures that: • Combine the strengths of LLMs (text generation, pattern recognition) with other approaches (symbolic reasoning, knowledge representation, causal inference). • Prioritize deep understanding and reliable reasoning over mere textual fluency. • Enable proactive analysis, goal-oriented action, and metacognition. In the following sections, we will outline the principles of such an architecture and present a vision for a future where AI can truly partner with lawyers to enhance the practice of law. 7. Principles of Agentic AI for Legal Reasoning: Moving beyond the "stochastic parrot" paradigm requires a fundamental shift towards agentic AI – systems that can act autonomously, reason strategically, and pursue goals in the legal domain. This is not simply about adding more layers or data to existing LLMs; it's about designing AI systems with fundamentally different capabilities. We propose the following core principles for agentic AI in legal reasoning: 7.1. Proactive Analysis (Beyond Question Answering): • Current LLMs: Primarily reactive. They respond to specific prompts or questions. They don't analyze a situation unless explicitly asked to do so. • Agentic AI: Should be proactive. Upon receiving a case file (facts, documents, relevant laws), it should automatically: o Identify the key legal issues. o Extract relevant facts and relationships. o Formulate potential arguments and counterarguments. o Assess the strengths and weaknesses of each side. o Propose a preliminary legal strategy. • Analogy: A lawyer doesn't just wait for the client to ask specific questions; they analyze the entire situation and anticipate potential problems. • Our approach: As it was shown in previous sections, the AI was able to develop a proactive approach, using the iterative method. 7.2. Goal-Oriented Reasoning (Planning and Strategy): • Current LLMs: Lack a concept of goals beyond generating coherent text. They don't plan or strategize. • Agentic AI: Should have explicit goals (e.g., win the case, negotiate a favorable settlement, draft a valid contract). It should be able to: o Define sub-goals that contribute to the main goal. o Plan a sequence of actions to achieve those goals. o Reason about the likely consequences of different actions. o Adapt its strategy as new information becomes available. • Analogy: A lawyer doesn't just write legal documents at random; they have a strategy for achieving their client's objectives, and they plan their actions accordingly. 7.3. Structured Legal Knowledge (Beyond Statistical Patterns): • Current LLMs: "Learn" about law from statistical patterns in text data. They don't have a conceptual understanding of legal rules, principles, or relationships. • Agentic AI: Needs a structured representation of legal knowledge, which could include: o Ontologies: Formal definitions of legal concepts and their relationships (e.g., "contract," "breach," "damages"). o Knowledge Graphs: Networks that connect legal rules, precedents, and factual situations. o Rule-Based Systems: Explicit representations of legal rules in a logical format (e.g., "If X and Y, then Z"). • Analogy: A lawyer doesn't just memorize legal texts; they understand the structure of the legal system, the relationships between different areas of law, and the underlying principles that inform legal rules. 7.4. Metacognition and Self-Evaluation (Reasoning about Reasoning): • Current LLMs: Have limited ability to monitor or evaluate their own reasoning. They can generate text, but they don't "know what they don't know." • Agentic AI: Should be capable of metacognition – "thinking about thinking." It should be able to: o Assess the confidence in its own conclusions. o Identify potential weaknesses in its own arguments. o Recognize gaps in its knowledge. o Seek additional information when necessary. o Learn from its mistakes. • Analogy: A good lawyer constantly reflects on their own reasoning, anticipates potential challenges, and adjusts their strategy accordingly. They are aware of their own limitations and seek advice or further information when needed. • Our approach: The AI, was able to identify some flaws in the reasoning, and to suggest changes. 7.5 Contextual Awareness (The world beyond the text): • Current LLM: Can be easily fooled by changes in the context. • Agentic AI: Should be able to understand the context, to avoid making mistakes. 7.6 Causal Reasoning: • Current LLMs: Struggle with causal reasoning. • Agentic AI: Should be able to evaluate, not only correlations, but have some notion of cause and effect. Conclusion (of this section): These principles represent a significant departure from the current paradigm of LLM development. They require a shift from pattern recognition to genuine understanding, from reactive responding to proactive reasoning, and from text generation to goal-oriented action. Building AI systems that embody these principles is a major challenge, but it is a challenge that must be met if we are to realize the full potential of AI in law. It´s not about making a bigger LLM, it´s about making a smarter AI. 8. Legalito: A Step Towards Agentic AI in Argentina • Brief: Legalito (legalito.ar) is presented not as a fully realized agentic AI system, but as a practical example of how technology can begin to address the challenges of legal practice in Argentina, and as a potential platform for future development. The focus is on complementing human expertise, not replacing it. While the principles of agentic AI outlined above represent a long-term vision, it's important to recognize that progress is already being made in applying AI to real-world legal problems. The Legalito platform (legalito.ar), developed in Argentina, provides a concrete example of how technology can be used to enhance and democratize access to legal information and services. It´s important to be clear: Legalito, in its current form, is not a fully autonomous legal AI agent. It doesn't reason about complex legal cases, formulate legal strategies, or represent clients in court. However, it does embody some of the principles of agentic AI, and it points towards a future where AI can play a more significant role in legal practice. How Legalito Embodies (Partially) Agentic Principles: 1. Proactive Information Gathering (Limited): o Legalito's chatbot and document analysis tools can proactively identify some relevant legal information based on user input. For example, it can guide users through a series of questions to determine their legal needs or identify key clauses in a contract. o Limitation: This is still largely based on pre-programmed rules and keyword matching, not on deep understanding of legal concepts. 2. Structured Knowledge (Partial): o Legalito has access to a database of legal information (laws, regulations, etc.). This information is organized in a way that makes it easier to find than simply searching the web. o Limitation: This is not a formal knowledge representation in the sense of an ontology or knowledge graph. It's more like a well-organized library than a reasoning engine. 3. Goal-Oriented Assistance (Basic): o Legalito can help users with specific legal tasks, such as drafting a basic legal document or finding a lawyer. o Limitation: The "goals" are predefined and relatively simple. Legalito cannot formulate its own legal strategies or adapt to complex, unforeseen situations. • Our approach: The development of the chatbot was a clear example of iterative learning. Legalito's Current Role: Legalito's primary value currently lies in: • Improving Access to Justice: Making legal information and basic legal services more accessible to the general public, especially those who cannot afford a lawyer. • Empowering Citizens: Providing citizens with the tools they need to understand their legal rights and obligations. • Streamlining Legal Processes: Automating routine tasks and reducing the workload for lawyers. • Complementing, Not Replacing, Lawyers: Legalito is designed to assist lawyers, not to replace them. It can handle simple tasks, freeing up lawyers to focus on more complex and strategic work. Legalito's Future Potential: Legalito could serve as a platform for developing and deploying more advanced agentic AI capabilities in the future. For example: • Integration with a Reasoning Engine: Legalito could be integrated with a symbolic reasoning engine that could analyze legal rules and apply them to specific fact patterns. • Natural Language Understanding: Improved natural language understanding capabilities could allow users to interact with Legalito in a more natural and intuitive way. • Personalized Legal Advice: Legalito could potentially provide personalized legal advice based on a user's specific situation and legal needs (with appropriate disclaimers and safeguards). • Automated Document Generation: Legalito could generate more complex legal documents (e.g., briefs, motions) based on user input and legal reasoning. Conclusion (of this section): Legalito represents a valuable step towards making legal services more accessible and efficient in Argentina. While it is not a fully autonomous legal AI agent, it demonstrates the potential of technology to transform the legal profession. As AI technology continues to evolve, platforms like Legalito could play an increasingly important role in bridging the gap between the promise of AI and the reality of legal practice. It also shows how, even in its current state, AI can improve and help both lawyers and citizens. 9. Dialogue as a Method: Exploring the Possibilities and the limitations This part will highlight the value of our conversation as a way to explore the complexities of AI and legal reasoning, illustrate the limitations of current LLMs, and generate ideas for future development. This article itself is not just a presentation of conclusions; it's a record of a journey. The dialogue between a lawyer (with practical experience in the field and in developing AI-powered legal tools) and an advanced LLM (Gemini 2.0 Pro Experimental 02-05) was instrumental in shaping the ideas presented here. This conversational approach, we believe, offers a valuable method for exploring the intersection of AI and law. It allows us to show the AI reasoning (or lack thereof) in real-time, and how the interaction with a human expert can lead to a deeper understanding of the challenges. 9.1. The Socratic Method in the Digital Age: The format of our interaction mirrors, in some ways, the Socratic method – a form of inquiry and discussion based on asking and answering questions to stimulate critical thinking and to illuminate underlying presumptions. • The Lawyer's Role: The lawyer acted as the questioner, probing the AI's understanding of legal concepts, challenging its assumptions, and pushing it to go beyond superficial answers. The lawyer provided the context, the real-world legal expertise, and the critical judgment that the AI lacked. • The AI's Role: The AI acted as a respondent, attempting to answer the lawyer's questions, generate text, and apply its knowledge to the problem at hand. But, crucially, the AI also served as a mirror, reflecting the limitations of current LLM technology. 9.2. "Coding" in Natural Language: An Analogy: One of the key insights that emerged from our dialogue was the analogy between writing a legal argument and writing code. While seemingly different, both activities share fundamental similarities: • Formal Systems: Both legal language and programming languages are formal systems with specific rules of syntax and semantics. • Precision and Clarity: Both require precision and clarity to avoid ambiguity and ensure correct interpretation. • Logical Structure: Both involve constructing logical sequences to achieve a desired outcome. A legal argument, like a computer program, must be internally consistent and logically sound. • Goal-Oriented: Both are goal-oriented. A program is written to perform a specific task; a legal argument is constructed to achieve a specific legal outcome. • Debugging: Both processess can be described as iterative. Just as a programmer uses code to instruct a computer, a lawyer uses language to "instruct" a judge (or other legal decision-maker). The lawyer's "code" consists of: • Facts: The "data" of the case. • Laws and Precedents: The "rules" or "functions" that govern the case. • Arguments: The "program" that combines facts and rules to reach a desired conclusion. This analogy helped us to understand why LLMs, which are primarily trained to generate text based on statistical patterns, struggle with legal reasoning. They can mimic the "syntax" of legal language, but they lack the deep understanding of the underlying logic and the ability to construct a truly coherent and persuasive argument. They are like a compiler that can check for syntax errors but cannot guarantee that the code will actually work as intended. 9.2. Illustrating the Learning Process: Through selected excerpts from our conversation (adapted and presented here in a concise form), we can illustrate how the dialogue led to a deeper understanding of the challenges and possibilities of AI in law: • Initial Assumptions: The AI, at the beginning, approached legal reasoning as a text generation task. It could produce text that looked like a legal argument, but it often missed key nuances or made logical errors. o "For example, when initially asked to contest the exceptions, the AI focused primarily on the LCT, neglecting the crucial aspect of 'stability' in public employment. It was through iterative questioning that the AI began to grasp the significance of this concept and its implications for the case." • Identifying Limitations: The lawyer's questions and challenges forced the AI to confront its own limitations. The AI could not simply rely on pattern recognition or statistical correlations; it had to engage with the meaning of legal concepts and the logic of legal arguments. o "The AI's initial attempts to define 'stability' were simplistic and text-book based. It struggled to apply the concept to the specific facts of the transfer from a provincial entity to a national one. This highlighted the AI's lack of contextual understanding and its inability to reason about the legal consequences of administrative decisions." • Developing New Ideas: The dialogue also served as a catalyst for new ideas. The analogy of "coding" in natural language, for example, emerged from our conversation and helped to clarify the precision and structure required for legal reasoning. o "The discussion about the limitations of current LLMs led to the analogy of 'stochastic parrots,' which vividly illustrates the difference between mimicking language and understanding it. This, in turn, sparked the exploration of alternative architectures for legal AI." • Iterative Refinement: The process of writing the article itself – drafting, revising, discussing, and refining – mirrored the iterative nature of legal reasoning. The AI's responses became more sophisticated and more relevant as the conversation progressed, reflecting a form of "learning" driven by human feedback. o "The process of contest the exceptions, as seen in previous examples, can be described as iterative. The AI hability to generate text, improved trough the interaction with a laywer, who identified flaws and asked for corrections. This process, in a sense, can be compared to the actual work of law professionals". 9.3. The Value of Collaboration: Our dialogue demonstrates the potential of human-AI collaboration in the legal field. • Complementary Strengths: The lawyer brought legal expertise, critical judgment, and real-world experience. The AI brought computational power, access to vast amounts of data, and the ability to generate text quickly and efficiently. • Synergy: The combination of these strengths led to a deeper understanding of the problem and to the development of more innovative solutions than either the lawyer or the AI could have achieved alone. 9.4 The "black box" problem and the need of human interaction: • "As it was pointed before, LLM are a black box, that is, a system whose internal process can´t be explained.* • "The iterative method, shows how human interaction is a key factor, to guide and improve an AI reasoning, until a satisfactory result is achived. Conclusion (of this section): The dialogue format is more than just a stylistic choice. It's a method for exploring complex issues, revealing hidden assumptions, and generating new insights. It highlights the limitations of current AI technology, but also points towards the potential of human-AI collaboration in the future of law. By making the process of discovery visible, we hope to encourage further discussion and innovation in this rapidly evolving field. 10. Conclusion: A Call for Collaboration and Innovation This article, born from a dialogue between a practicing lawyer and an advanced language model, has explored the exciting potential and the significant limitations of applying current AI technology to the complex world of legal reasoning. We have argued that while Large Language Models (LLMs) demonstrate impressive capabilities in generating text and mimicking human language, they fall short of the deep understanding, contextual awareness, and logical reasoning required for true legal expertise. The prevailing paradigm of "more of the same" – larger models, more data – is not sufficient to bridge this gap. We are, as Watzlawick might put it, caught in a loop of applying an "attempted solution" that, while showing some progress, perpetuates the fundamental problem. 10.1. Key Takeaways: • LLMs as "Stochastic Parrots": Current LLMs primarily operate by predicting the statistically most likely sequence of words, based on vast amounts of training data. This "pattern recognition" is not equivalent to genuine understanding or reasoning. • Legal Reasoning Requires More: Legal practice demands logical deduction and induction, contextual understanding, the ability to handle ambiguity, the construction of persuasive arguments, and the anticipation of counterarguments. It's a goal-oriented activity, and not a mere combination of words. • The Stability Example: The case study of stability in public employment in Argentina highlights the critical need for nuanced, context-sensitive reasoning that goes beyond the literal text of legal provisions. • Benchmarks are Insufficient: Current benchmarks for evaluating LLMs often fail to capture these crucial aspects of legal reasoning, focusing instead on surface-level text generation skills. • Architectural Innovation Needed: We must move beyond the "more of the same" trap and embrace architectural innovation in AI. This means exploring hybrid systems that combine the strengths of LLMs with symbolic reasoning, knowledge representation, and causal inference. • Agentic AI as a Goal: We advocate for a shift towards agentic AI – systems that can proactively analyze legal information, formulate legal strategies, reason based on explicit goals, and even exhibit a degree of metacognition (awareness of their own reasoning process). • Dialogue as a Method: Our own conversation, presented in part within this article, exemplifies the value of iterative, collaborative exploration in understanding the challenges and possibilities of AI in law. It also highlights how a human-AI partnership can lead to deeper insights than either could achieve alone. • The "black box" problem: LLM, as it is, is a system whose internal process cannot be understood. 10.2. A Call to Action: The future of AI in law is not predetermined. It is up to us – researchers, developers, lawyers, policymakers, and the broader public – to shape that future. We call for: • Increased Investment in Fundamental Research: We need more funding and resources dedicated to exploring alternative architectures for AI, moving beyond the dominant paradigm of LLMs. • Development of More Realistic Benchmarks: We need new benchmarks that truly evaluate legal reasoning capabilities, not just text generation skills. • Interdisciplinary Collaboration: We need closer collaboration between computer scientists, legal professionals, philosophers, and cognitive scientists to tackle the unique challenges of legal AI. • Ethical Considerations: We need to carefully consider the ethical implications of increasingly sophisticated AI in law, ensuring fairness, transparency, and accountability. • Open Discussion and Debate: We need a broad and open discussion about the role of AI in the legal system, involving all stakeholders. 10.3. The Path Forward: The journey towards AI systems that can truly reason like lawyers will be challenging, but the potential rewards are immense. Imagine: • Increased Access to Justice: AI-powered tools that make legal information and assistance available to everyone, regardless of income or location. • More Efficient Legal Processes: AI systems that automate routine tasks, freeing up lawyers to focus on more complex and strategic work. • Better Legal Decision-Making: AI systems that help judges and lawyers make more informed, consistent, and just decisions. • Better legal assistance for lawyers: AI systems that can help laywers to make better analysis of the cases. This is not about replacing lawyers with robots. It's about empowering lawyers with better tools and creating a more just and efficient legal system for all. It's about augmenting human intelligence with artificial intelligence, combining the strengths of both to achieve outcomes that neither could achieve alone. The development of Legalito, and its integration with AI tools, represents a small but meaningful step in that direction. This article, we hope, is another. We invite you to join the conversation and help build the future of law. ________________________________________ While this article has focused on the challenges and opportunities of AI in the legal profession, the issues we have raised are not unique to law. The limitations of current LLMs – their reliance on pattern recognition, their lack of deep understanding, their difficulty with complex reasoning – are equally relevant to any field that requires expert knowledge, critical thinking, and the ability to make informed decisions based on incomplete or ambiguous information. Whether it's a doctor diagnosing a patient, an engineer designing a structure, a scientist interpreting experimental data, a financial analyst assesing a risk, or a policymaker crafting legislation, the need for AI systems that can truly reason, understand, and adapt is paramount. The principles of agentic AI that we have outlined – proactive analysis, goal-oriented reasoning, structured knowledge representation, and metacognition – are not just legal principles; they are general principles of intelligent action that should guide the development of AI in all domains. The dialogue presented here, therefore, serves as a starting point for a broader conversation about the future of AI and its role in assisting professionals to perform complex tasks, and society in general. About the Authors: • DARIO JAVIER RAMIREZ, Lawyer, founder and CEO of Legal-it-Ø. • Gemini 2.0 Pro Experimental 02-05 (assisted by Darío Javier Ramírez): An experimental advanced language model developed by Google AI, used in this project for collaborative writing and legal reasoning exploration.