Agentic AI currently looks like a much more difficult tool to integrate into legal operations than standard Generative AI; yet experimenting with it may help firms understand their own operations in ways no other tool can
Key insights:
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Agentic AI proports to be the next step in legal technology — With its ability to be a proactive, rather than responsive asset agentic AI is essential; however, it also comes with hurdles beyond even those of non-agentic AI.
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There are benefits in teaching agentic AI your work patterns — Attempting to teach an agent your workflows force you to identify hidden assumptions, undocumented steps, and inconsistencies that can present long-term inefficiencies and risks.
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Build a rigorous sandbox for agentic experimentation — Such a sandbox creates reusable infrastructure for testing any emerging technology, a capability that will outlast any single tool.
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As AI races ahead, some parts of the legal tech industry are already gazing past its still-new generative AI (GenAI) tools and asking, “Okay, what’s next?” That would be agentic AI — that class of GenAI capable of planning, reasoning, and executing multi-step workflows with less human involvement. Powerful? Potentially. Intriguing? Certainly. Ready for legal practice? That’s less certain.
But here’s the thing, even technologies that aren’t perfectly ready for production can be worth exploring. And agentic AI, for reasons that have little to do with its actual outputs, may be one of them. Before we get to why, let’s be fair about what’s driving the excitement.
The appeal is real
Today’s GenAI tools wait to be prompted and thus are reactive by design. Agentic AI promises something different: systems that could monitor approaching deadlines, flag a case that just got overruled, or prep you before a client call by pulling relevant documents unprompted. The shift from a tool you use to an assistant that anticipates is what’s captured imaginations, and understandably so.
And in adjacent fields, that promise may land sooner. Tax work, for example, is fundamentally about applying a codified set of rules to structured inputs, such as financial statements, forms, and numerical data that AI can parse reliably. Error correction mechanisms exist — file a return with a mistake, and you can amend it. Legal work, by contrast, deals in narrative ambiguity, judgment calls, and adversarial contexts in which someone on the other side is actively looking to exploit errors. That’s a recipe for friction.
Why legal is different
Indeed, a major reason agentic AI introduces a fundamentally different risk profile for law firms is because it shifts AI from answering to acting. The moment a system can initiate tasks, chain decisions, access tools, or manipulate data flows, it collides head-on with the core pillars of legal practice: auditability, accountability, confidentiality, and control.
Unlike GenAI which might produce a flawed clause, an agentic model might produce a flawed clause and then email it to the client, file it with the court, and even send it to opposing counsel. This is not theoretical — it’s an inherent property of the technology.
Law firms operate in environments in which even small deviations can have outsized consequences. A missed filing deadline, an unlogged modification, or a misrouted document can trigger cascading risk. Every action requires traceability, and every outcome must be defensible. An agentic system might creatively decide to restructure a workflow to speed up processing and in doing so open the door to malpractice.
The irony is that agentic AI’s very strength — its ability to carry out multi-step tasks autonomously — is exactly what makes it so difficult to safely deploy in legal contexts. Even where the work is operational — docketing, conflict checks, matter intake — firms already have deterministic automation or configurable workflow engines that perform the task with zero ambiguity.
Beyond introspection, agentic AI experimentation also builds the one muscle that law firms almost universally lack and increasingly need: the ability to rapidly test, evaluate, and iterate on emerging technology.
Agentic AI’s flexibility is an advantage only in environments that welcome flexibility; however, most legal environments do not. Therefore, finding a niche which Agentic AI can fit into will be more difficult than it was for non-agentic systems and the implementation efforts thus a greater, more complex undertaking.
Let’s gain some perspective. The year is (nearly) 2026 and we’ve barely finished unboxing the first wave of GenAI and haven’t yet gotten it to work at industry scale, let alone worked out the kinks. Most law firms are still on step one, learning how to use the basics effectively — building prompting skills, experimenting with drafting tools, sorting out hallucination mitigation, and figuring out how to communicate with clients about the firm’s AI use.
Agentic AI is not simply GenAI, but more of it, it’s a step far beyond what most large law firms are ready for.
Why experimenting still matters
Counterintuitively, the very difficulty of agentic systems is what makes them worth exploring. Teaching an agent how to operate inside your workflows forces you to spell out the steps you normally gloss over, clarify requirements you’ve never written down, and confront inconsistencies that human judgment quietly patches every day. A surprising number of legal workflows only function smoothly because experienced professionals intuitively bridge gaps — they know where the data actually lives, understand which steps aren’t formally documented, or recognize that two processes technically contradict each other but will work out in practice.
Once you task an agent with following those same workflows, all of those hidden assumptions, tacit dependencies, and buried bottlenecks surface immediately.
And this visibility is where the real payoff lies. When those messy realities are exposed, firms can finally address long-standing issues that have quietly eroded efficiency for years — documentation improves, process maps get corrected, redundant steps are removed or automated, and succession plans are updated so vital cultural knowledge isn’t lost. In other words, the act of preparing a system to learn your processes becomes the catalyst for cleaning, strengthening, and modernizing those very processes.
As the old saying goes, There is no better way to learn than by teaching.
Beyond introspection, agentic AI experimentation also builds the one muscle that law firms almost universally lack and increasingly need: the ability to rapidly test, evaluate, and iterate on emerging technology.
Law firms operate in environments in which even small deviations can have outsized consequences — a missed filing deadline, an unlogged modification, or a misrouted document can trigger cascading risk.
For decades, legal innovation has been slow, risk-averse, and highly siloed — a reasonable stance when change came in predictable, manageable waves. However, today’s pace of technological evolution, AI-driven or otherwise, no longer respects those timelines. Firms that cannot safely experiment will be at a structural disadvantage, not because agentic AI will replace lawyers, but because the firms that learn how to run experiments will be able to adapt faster to anything new.
And this is where the payoff becomes really tangible. Building a rigorous, well-governed sandbox for agentic AI — with clear guardrails, monitoring, risk controls, and evaluation methods — creates a reusable asset. Once constructed, that environment can be repurposed for testing new drafting tools, workflow systems, client-facing applications, or even internal process changes. Each experiment will teach teams how to scope pilots, measure outcomes, identify weak spots in data or process flows, and rapidly iterate without disrupting client work. Over time, this turns into a cycle: faster pilots, cleaner requirements, more precise risk assessments, and ultimately more reliable adoption decisions.
Even if a firm’s agentic AI experimentation finds the technology isn’t yet ready, the discipline that the firm could potentially develop while testing it becomes a foundation for future innovation — a capability that will matter far more than any single tech tool.
You can find out more about the challenges and opportunities of working with agentic AI here
