In the 20th century, mastering “thinking like a lawyer” meant developing a rigorous, precedent-driven mindset. Today, we find ourselves on the cusp of yet another evolution in legal thinking—one driven by agentic AI models that can plan, deliberate, and solve problems in ways that rival and complement human expertise.

In this article, we’ll explore how agentic reasoning powers cutting-edge AI like OpenAI’s o1 and o3, as well as DeepSeek’s R1 model. We’ll also look at a technical approach, the Mixture of Experts (MoE) architecture, that makes these models adept at “thinking” through complex legal questions. Finally, we’ll connect the dots for practicing attorneys, showing how embracing agentic AI can boost profitability, improve efficiency, and elevate legal practice in an ever-competitive marketplace.

1. The Business of Law Meets Agentic Reasoning

Legal practice is as much about economics as it is about jurisprudence. When Richard Susskind speaks of technology forcing lawyers to reconsider traditional business models, or when Ethan Mollick highlights the way AI can empower us with a co-inteligence, they’re tapping into the same reality: law firms are businesses first and foremost. Profit margins and client satisfaction matter, and integrating agentic AI is quickly becoming a competitive imperative.

Still, many lawyers hesitate, fearing automation will erode billable hours or overshadow human expertise. The key is to realize that agentic AI, tools that can autonomously plan, analyze, and even execute tasks, don’t aim to replace lawyers. Instead, they empower lawyers to practice at a higher level. By offloading rote tasks to AI, legal professionals gain the freedom to focus on nuanced advocacy, strategic thinking, and relationship-building.

2. A Quick Tour: o1, o3, and DeepSeek R1

OpenAI’s o1: Laying the Agentic Foundation

Introduced in September 2024, o1 marked a significant leap forward in AI’s reasoning capabilities. Its defining feature is its “private chain of thought,” an internal deliberation process that allows it to tackle problems step by step before generating a final output. This approach is akin to an associate who silently sketches out arguments on a legal pad before presenting a polished brief to the partner.

This internal “thinking” has proven especially useful in scientific, mathematical, and legal reasoning tasks, where superficial pattern-matching often falls short. The trade-off? Increased computational demands and slightly slower response times. But for most law firms, especially those dealing with complex litigation or regulatory analysis, accuracy often trumps speed.

OpenAI’s o3: Pushing Boundaries

Building on o1, o3 arrived in December 2024 with even stronger agentic capabilities. Designed to dedicate more deliberation time to each query, o3 consistently outperforms o1 in coding, mathematics, and scientific benchmarks. For lawyers, this improvement translates to more thorough statutory analysis, contract drafting, and fewer oversights in due diligence.

One highlight is o3’s performance on the Abstraction and Reasoning Corpus for Artificial General Intelligence (ARC-AGI). It scores nearly three times higher than o1, underscoring the leap in its ability to handle abstract reasoning, akin to spotting hidden legal issues or anticipating an opponent’s argument.

DeepSeek R1: The Open-Source Challenger

January 2025 saw the release of DeepSeek R1, an open-source model from a Chinese AI startup. With performance on key benchmarks (like the American Invitational Mathematics Examination and Codeforces) exceeding o1 but just shy of o3, DeepSeek R1 has quickly attracted viral attention. Perhaps its biggest draw is cost-effectiveness: it’s reportedly 90-95% cheaper than o1. That kind of pricing is hard to ignore, especially for smaller firms or legal tech startups that need powerful AI without breaking the bank. DeepSeek R1’s open-source license also opens the door to customization: imagine a specialized “legal edition” any firm can adapt.

The market impact has been swift: DeepSeek R1’s launch catapulted its associated app to the top of the Apple App Store and triggered a sell-off in AI tech stocks. This frenzy underscores a critical lesson: the world of AI is volatile, competitive, and global. Law firms shouldn’t pin their entire strategy on a single vendor or model; instead, they should stay agile, ready to explore whichever AI solution best fits their needs.

3. How Agentic Reasoning Actually Works

All these models—o1, o3, and DeepSeek R1—share a common thread: agentic reasoning. They’re built to do more than just respond; they deliberate. Picture an AI “intern” that doesn’t just copy-and-paste from a template but weighs the merits of different statutes, checks your prior briefs, and even flags contradictory language before you finalize a contract.

But how do they manage this level of autonomy under the hood? Enter the Mixture of Experts (MoE) architecture.

Mixture of Experts (MoE) Architecture

1. Experts: Think of each expert as a specialized “mini-model” focusing on a single domain—perhaps case law parsing, contract drafting, or statutory interpretation.
2. Gating Mechanism: This is the brains of the operation. Upon receiving an input (e.g., “Draft a motion to compel in a federal product liability case”), the gating system selects the subset of experts most capable of handling that task.

The process is akin to sending your question to the right department in a law firm: corporate experts for an M&A agreement, litigation experts for a discovery motion. By activating only the relevant experts for a given task, the AI remains computationally efficient, scaling easily without ballooning resource needs. This sparse activation mirrors an attorney’s own approach to problem-solving; you don’t bring in your tax partner for a maritime dispute, and you don’t put your entire legal team on every single project.

For agentic reasoning, MoE models shine because they allow the AI to break down multi-faceted tasks into manageable chunks, using the best “sub-models” for each piece. In other words, the AI can autonomously plan which mini-experts to consult, deliberate internally on their advice, and then execute a cohesive final output, much like a senior partner synthesizing input from various practice groups into one winning brief.

4. Practical Impacts on Legal Workflows

Research and Drafting

Lawyers spend countless hours researching regulations and precedents. With agentic AI, that time shrinks dramatically. For instance, an MoE-based system could route textual queries to the “case law expert” while simultaneously consulting a “regulatory expert.” The gating mechanism ensures each question goes to the sub-model best suited to answer it. That means more accurate, tailored research in less time.

Document Review and Due Diligence

High-stakes M&A deals or massive litigation cases involve reviewing thousands of pages of documents. Agentic AI can quickly triage which documents to flag for deeper human review, finding hidden clauses or issues that might otherwise take an associate weeks to spot. The result? Faster, cheaper due diligence that can be billed in alternative ways: flat fees, success fees, or other value-based structures, enhancing client satisfaction and firm profitability.

Strategic Advisory

Perhaps the most exciting application is strategic planning. By running different hypothetical arguments or settlement options through an agentic model, attorneys can gain insights into possible outcomes. Imagine a “simulation-expert” sub-model that compares potential trial outcomes based on past jury verdicts, local court rules, and judge profiles. While final decisions rest with the lawyer (and client), AI offers a data-driven edge in deciding whether to settle, proceed, or counter-offer.

5. Profitability: Beyond the Billable Hour

One of the biggest hurdles to adopting AI is the fear that automated tasks will reduce billable hours. But consider how value-based billing or flat-fee arrangements can transform the equation. If AI cuts a 10-hour research task down to 2, you can offer clients a predictable cost and still maintain or even improve your margin. Clients often prefer certainty, and they value speed if it means resolving matters sooner.

Additionally, adopting agentic AI can allow your firm to take on more cases or offer new services, like real-time compliance monitoring or rapid contract generation. Scaling your practice to handle more volume without expanding headcount can be a powerful revenue driver.

6. The Human Element: Lawyers as Conductors

Agentic AI models are not a substitute for the judgment, empathy, and moral responsibility that define great lawyering. Rather, think of AI as your personal ensemble of experts, each playing a specialized instrument. You remain the conductor, guiding the orchestra to create a harmonious legal argument or transaction.

If anything, the lawyer’s role becomes more vital in an AI-driven world. Your expertise ensures the AI’s recommendations make sense in the real world of courts, regulations, and human relationships. Your ethical obligations and professional standards guarantee that client confidentiality is safeguarded, conflicts of interest are managed, and justice is served.

Closing Thoughts

The real paradigm shift here comes from recognizing how AI agents, powered by a Mixture of Experts architecture, can function like a fully staffed legal team, all contained within a single system. Picture a virtual army of associates, each specialized in key practice areas, orchestrated to dynamically route tasks to the right “expert.” The result? A law firm that can harness collective knowledge at scale, ensuring top-notch work product and drastically reducing turnaround times.

Rather than replacing human talent, this approach enhances it. Lawyers can channel their energy into strategic thinking, client relationships, and creative advocacy, those tasks that define the very essence of the profession. Meanwhile, agentic AI handles heavy lifting in research, analysis, and repetitive drafting, enabling teams to serve more clients, tackle more complex matters, and ultimately become more impactful and profitable than ever before.

Far from an existential threat, these AI advancements offer us the freedom to practice law at its best, delivering deeper insights with greater efficiency. In embracing these technologies, we build a future where legal professionals can make more meaningful contributions to both their firms and the broader society they serve.