Agentic AI represents a fundamental shift in how legal teams approach complex investigations and document review—moving from tools that answer single questions to autonomous systems that pursue entire goals without human prompting at each step. Unlike traditional generative AI that requires a new prompt for each task, agentic AI can call on multiple tools, query data sources, assess interim results, and adjust course all within a single workflow, fundamentally changing how in-house counsel allocates attorney time and budgets. What's the Real Difference Between Agentic AI and the ChatGPT You Already Use? The distinction matters more than it might seem at first. Generative AI answers a specific question when prompted—you ask it to summarize a document, and it does. Agentic AI pursues a defined goal autonomously, making a sequence of decisions and taking a series of actions to get there, without requiring a human prompt at each step. Think of it as the difference between asking an assistant to summarize one email versus deploying an agent that can investigate an entire dataset, surface key themes, flag anomalies, and report findings without step-by-step direction. This distinction has real implications for how legal teams work. Michael LaBrie, Director of Cloud Solution Implementation at OpenText, which has been deploying AI in legal technology for more than two decades, explains the progression: "Generative AI answers a specific question when prompted. Agentic AI pursues a defined goal autonomously, making a sequence of decisions and taking a series of actions to get there, without requiring a human prompt at each step." For legal operations, this means the difference between asking a language model to summarize one document and deploying an agent that can investigate an entire dataset, surface key themes, flag anomalies, and report findings without step-by-step human direction. How Three Generations of AI Have Evolved in Legal Technology Understanding where agentic AI fits requires stepping back to see how legal technology has progressed over the past two decades. OpenText has been applying AI to legal systems since 2002, and the evolution tells a clear story about what's possible now. - Rules-Based AI (First Generation): Deterministic systems that follow instructions precisely, like Boolean keyword searches. If a document contains a specific word, it is flagged. Reliable, but limited in scope and unable to understand context or nuance. - Machine Learning (Second Generation): Systems that learn patterns rather than following rigid rules. They can recognize that different phrases relate to the same concept, surface likely relevant documents, and identify people, places, or emotional tone within a document corpus. Document summaries at this stage are essentially sophisticated highlights, though not synthesized analysis. - Generative AI (Third Generation): Rather than recognizing patterns, these systems create content. A generative AI summary is not a collection of extracted phrases but a contextualized narrative, like the difference between a list of keywords and a well-written headnote. Agentic AI builds on this generative foundation and represents where the technology is heading now. It combines the reasoning capabilities of generative AI with the ability to act autonomously across complex workflows. Where Does Agentic AI Create Real Value for In-House Counsel? For general counsel managing budgets and outside counsel relationships, agentic AI delivers measurable leverage in workflows that have historically been difficult to systematize. Consider a complex commercial litigation matter where counsel needs to reconstruct a timeline of executive communications across thousands of emails, chat logs, and board minutes. Traditionally, that falls to a junior associate or contract reviewer working through documents manually, flagging items for a senior attorney to assess in context. An agentic AI can handle that investigative layer—pulling relevant documents, identifying patterns, cross-referencing dates and custodians, and surfacing a draft chronology—before a senior attorney ever touches the file. The result isn't just faster document review. It's that experienced attorneys spend less time gathering and organizing information and more time doing what they're actually trained to do: analyzing what the facts mean and advising clients on what to do next. Steps to Implementing Agentic AI in Your Legal Operations - Identify High-Volume Investigative Tasks: Start with workflows that require multi-step reasoning and where the investigative path isn't linear, such as corporate internal investigations or complex eDiscovery matters where the direction depends on what turns up. - Define Clear Boundaries and Oversight: Agentic AI performs best where tasks require multi-step reasoning and where the context is complex, but legal teams should establish clear boundaries around decision-making authority and maintain human review of critical findings. - Integrate with Existing Legal Platforms: Deploy agentic AI within enterprise legal systems that already contain your document repositories and case data, allowing the agent to access multiple tools and data sources within a single workflow. - Measure Cost and Time Savings: Track how much attorney time is freed up from information gathering and organization, and calculate the cost reduction compared to traditional contract review or junior associate hours. What Tasks Should Remain in Human Hands? Agentic AI performs best where tasks require multi-step reasoning, where the tools needed are not fully predictable in advance, and where the context is complex. This describes the investigative and analytical work that sits at the heart of most eDiscovery and internal investigation matters. However, legal teams should draw clear boundaries around where autonomous AI adds strategic value and where human judgment remains essential. Take a corporate internal investigation triggered by a whistleblower complaint. Outside counsel comes in with a set of names, a date range, and a general allegation. From there, the investigative path isn't linear—it depends on what turns up. An agent can autonomously work through that process: searching for relevant communications, identifying patterns, and flagging anomalies. But the interpretation of those findings, the assessment of credibility, and the strategic decisions about what to investigate next should remain with experienced attorneys who understand the broader business context and legal implications. The key insight for in-house counsel is that agentic AI doesn't replace attorney judgment—it amplifies it by handling the time-consuming investigative groundwork that currently consumes junior attorney and contract reviewer hours. By automating the information-gathering phase, agentic AI lets experienced attorneys focus on the analysis and advice that clients actually pay for, while reducing overall legal spend and accelerating case timelines.
