What Legal Ops Needs to Know About Generative AI and Agentic AI

A recent eDiscovery Today blog post discusses the difference between generative AI and agentic AI, focusing on the next phase of artificial intelligence (AI). The distinction increasingly shapes how legal ops professionals think about efficiency, defensibility, and the future of eDiscovery workflows. 

Generative AI accelerates human work by creating content — text, summaries, images, or analysis — but only when prompted. In the context of eDiscovery, this capability may include drafting first-pass legal hold notices, summarizing Slack threads or deposition transcripts, suggesting privilege log language, or proposing expanded search terms based on the data provided. 

Agentic AI, on the other hand, is designed to act autonomously. Given a goal, an agentic system can break that goal into steps, move across systems, execute tasks, monitor results, and adjust without constant prompting. In eDiscovery, that autonomy translates into end-to-end workflows. An agentic system can collect data from multiple collaboration and file platforms, initiate processing, monitor for errors, and deliver review-ready datasets. 

Furthermore, agentic AI can continuously analyze incoming documents, update timelines, and flag emerging risks as a matter evolves. It can also perform privilege quality control, identify miscoded documents, and uncover anomalies for human review. 

The practical takeaway is simple but significant. Generative AI helps legal teams think and write faster. Agentic AI helps legal systems act faster. Understanding the difference between generative AI and agentic AI is critical for legal ops professionals tasked with evaluating where automation ends and autonomous action begins.