Extract from Reveal’s article “eDiscovery AI: Agentic Review Reshapes Legal Teams”
How Agentic AI Is Transforming eDiscovery Review
For two years, the eDiscovery industry has measured generative AI by how well it answers a single prompt: summarize this document, flag this issue, draft this privilege note. That framing is already outdated. The more consequential shift is what happens when AI stops waiting for the next prompt and starts sequencing an entire review task on its own, checking its own outputs, and handing a defensible result to a reviewer for sign-off.
eDiscovery AI refers to the use of artificial intelligence, including generative and agentic models, to identify, classify, and analyze electronically stored information within a defensible discovery workflow. Agentic eDiscovery AI extends this idea further: instead of responding to one prompt at a time, an AI agent plans and executes a sequence of review tasks, such as triaging a data set, applying issue tags, and flagging privileged material, while keeping a reviewer in the loop for validation.
eDiscovery AI Is Moving from Single Prompts to Coordinated Workflows
Technology-assisted review automated a narrow task: predicting responsiveness from attorney-coded examples. Generative AI extended that by letting reviewers ask questions of a document set in plain language. Agentic AI is the next step, changing the unit of work from a single answer to a managed process.
