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 That framing is already outdated.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. Legal and compliance experts describe this as a move from generative AI performing isolated tasks to AI agents that coordinate identification, review, and production while preserving an audit trail, according to the ACEDS “Has Generative AI Made a Meaningful Contribution to E-Discovery?” analysis. A prompt that produces a good summary is useful; a system that plans a review strategy, executes it across a document population, and documents its own reasoning is a different category of tool. Reveal's analysis of what generative AI can and cannot do in legal workflows makes a related point: the value of GenAI in legal work depends on the workflow surrounding it, not the model alone.

This is also why prompt-driven GenAI review is increasingly discussed as a new standard alongside, rather than in place of, traditional TAR. Agentic systems build on that same foundation, adding orchestration on top of prompt-driven review.

The Review Bottleneck Agentic AI Is Built to Address

Document review remains the most expensive, least predictable phase of the discovery lifecycle for most legal departments. The 2025 State of the Industry Report found that a majority of surveyed professionals expect generative AI to create new review workflows, even though full-scale use still lags behind expectations, per the same ACEDS analysis. That gap between expectation and adoption is where agentic workflows can help, by giving legal teams a structured way to apply AI across a full review rather than one query at a time.

Early Case Assessment Becomes an Agentic Entry Point

Early case assessment eDiscovery work is a natural starting point for agentic AI because the task is already sequential: understand what data exists, gauge its relevance, and reduce volume before review begins. Alvarez & Marsal describes AI agents that categorize the severity of a matter, gather data across multiple systems, and produce a summary report for a senior investigator before a human reviewer opens a single document. Applied to early case assessment, this kind of agent can compress the identification and triage phase and reduce the volume of data that reaches paid review.

Privilege and PII Review Becomes a Structured, Repeatable Task

Privilege review is one of the highest-risk, highest-scrutiny tasks in any matter, and one of the clearest use cases for agentic workflows, because identifying a communication, assessing the relationship, applying a designation, and logging the reasoning is repeatable and auditable. Reveal's guidance on using AI for privilege review outlines how AI-assisted privilege workflows should be structured so every designation traces back to a documented rationale. Agentic AI extends that structure across an entire document population, while still routing edge cases to a human reviewer.

Discovery Management Software Must Support Multi-Agent Review

As review tasks move from single prompts to coordinated agent workflows, discovery management software has to change with them. Litigation discovery software built only for keyword search or single-turn GenAI prompts cannot support multiple agents triaging, tagging, and validating in parallel. Reveal's GenAI engine, Aji, is built around this kind of ask-and-answer, agent-driven document review, designed so legal teams can direct AI across a review rather than issuing one prompt and hoping the output holds up. That difference, between a single well-crafted prompt and a governed, repeatable process, is also the subject of Reveal's guide on moving from prompt and pray to precision with Aji.

Defensibility Is the Real Test for Agentic AI in Legal Review

Speed is the most visible benefit of agentic AI, but defensibility determines whether a legal department can rely on it. Every agentic decision, from an early case assessment triage call to a privilege designation, needs a documented rationale that can withstand a meet-and-confer discussion or judicial scrutiny. Alvarez & Marsal notes that the autonomous nature of agentic AI introduces a level of scrutiny beyond what generative AI alone required, and organizations deploying these agents need clear sign-off processes at the appropriate management level. PwC's overview of AI in eDiscovery and managed review reaches a similar conclusion: AI can streamline review, but human expertise remains necessary, not optional.

What Legal and Compliance Leaders Should Do Now

• Ask vendors how their AI plans and sequences tasks, not just how it answers a single prompt.

• Require documented reasoning for every AI-driven decision, including privilege designations and relevance calls.

• Start agentic adoption in early case assessment, where the risk of a wrong call is lower than in production decisions.

• Keep a reviewer at defined checkpoints, rather than treating agentic AI as a fully autonomous replacement for legal judgment.

The Shift Legal Teams Need to Plan For

Agentic AI does not remove the legal team from the review process. It changes what the team is reviewing: not individual documents one at a time, but the reasoning and output of a coordinated AI workflow applied across a matter. Legal departments that build the governance and sign-off practices for that shift now will be positioned to use agentic AI as it matures, rather than retrofitting oversight after adoption has outpaced it.

To see how a governed, agentic approach to review works in practice, schedule a demo with Reveal or contact the Reveal team.