I’ve been building software for long enough to know that when everyone starts talking about a new concept, half the time they’re using the term loosely. Agentic workflows are different. They’re not just buzzword material—they’re a genuine shift in how we can structure work, especially in fields like law where you’ve got repetitive, decision-heavy tasks that eat up hours.
When Everlaw announced their new agentic AI feature recently, rolling it out to firms like Bracewell and Vorys, it caught my attention not because it’s flashy, but because it solves a real problem I’ve seen law firms struggle with for years. Let me break down what’s actually happening here and why it matters.
Understanding What Agentic Really Means
First, let’s get the terminology straight. An “agentic” workflow isn’t just AI doing what you tell it to do. It’s AI that can break down a complex task, make decisions along the way, and adjust its approach based on what it finds. Think of it as the difference between a calculator and a consultant.
A calculator takes your input and spits out an answer. A consultant understands the problem, asks clarifying questions, considers multiple approaches, and recommends the best path forward. That’s the agential piece.
In legal work, this distinction matters enormously. Document review isn’t just about flagging keywords anymore. A real agentic system can:
- Assess relevance based on context and case strategy
- Identify inconsistencies across documents
- Make judgment calls about privilege and confidentiality
- Prioritize which materials matter most
- Adapt its approach as it learns more about the case
That’s what separates agentic workflows from traditional automation. The system isn’t just executing a fixed set of rules—it’s thinking through the problem space.
Where the Real Value Comes In
I’ve watched law firms manually manage document review for years. The process is brutal: paralegals and junior associates spend weeks reading through thousands of pages, making consistent judgment calls, and flagging things that matter. It’s necessary work, but it’s also precisely the kind of work that burns people out and eats budgets alive.
When you introduce agentic workflows into this picture, several things happen at once:
Speed increases dramatically. An AI agent can process and assess documents at a pace humans simply can’t match. Not perfectly—no tool is perfect—but at scale, getting to eighty percent of the insights in twenty percent of the time changes the economics of a case.
Consistency improves. Human reviewers get tired. They miss things. They apply standards differently depending on the time of day or how long they’ve been working. An agentic system doesn’t suffer from fatigue. It applies the same logic to document ten thousand that it applied to document one.
Quality control becomes possible. Because the AI is showing its reasoning—explaining why it flagged something or marked it privileged—senior lawyers can actually spot-check the work and refine the approach. This feedback loop makes the system better over time.
From a software perspective, this is one of the reasons tools like what Everlaw is building are technically interesting. They’re not just throwing a language model at a problem. They’re building systems that can be audited, refined, and improved based on how lawyers actually work.
The Practical Reality of Implementation
Here’s where I want to be honest about what I’ve learned from deploying tools at this level of complexity. Early access programs like the one Everlaw is running with firms like Bracewell and Vorys aren’t just about showing off new features. They’re about figuring out what actually works in the real world versus what looks good in a demo.
Integration is always trickier than it sounds. Legal tech doesn’t exist in isolation. It has to work with existing case management systems, security frameworks, and review protocols. Agentic workflows add another layer of complexity because the AI needs to understand not just documents, but the specific context and preferences of each firm using it.
That’s why these early partnerships are valuable. Firms like Bracewell and Vorys aren’t just testing software—they’re helping define what an agentic system should actually do in a law firm environment. They’re figuring out edge cases, flagging where the AI gets confused, and helping shape the product into something that actually fits how lawyers work rather than forcing lawyers to change how they work.
The other thing that matters: trust. Lawyers need to trust the tools they’re using, especially when those tools make decisions that affect client work. An agentic system has to be transparent enough that lawyers can understand why it made a call. That’s not a technical nice-to-have. It’s foundational.
What This Means for the Broader Picture
What Everlaw is doing with agentic workflows is part of a larger shift happening across professional services software. We’re moving past the idea that AI should replace humans and toward the idea that AI should make humans more effective.
For law firms, this could mean fewer hours burned on grunt work and more time spent on strategy, client relationships, and the parts of legal work that actually require judgment and experience. For clients, it means better quality work at more predictable costs.
That’s not hype. That’s just what happens when you build tools that work with how people actually think rather than against it.
Source: Everlaw Unveils New Agentic AI Feature, Review Management Offering | Law.com
Laura Bennett
Tom Harrington
Emma Clarke