How to Evaluate Agentic AI Tools for Litigation Work Beyond Fact Finding

Agentic AI has moved quickly from concept to vendor pitch, but the distance between what vendors market and what their tools deliver is significant. In June 2025, Gartner predicted that over 40% of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value, or inadequate risk controls, and estimated that only about 130 of the thousands of vendors marketing agentic capabilities actually deliver them. For legal teams, that distance matters more than in most industries. A tool that performs well in a demo can still fail when its output has to survive a meet-and-confer, a privilege challenge, or a motion to compel.

Interest is not the obstacle. Document review ranked as the leading generative AI use case among legal professionals at 77%, according to LawSites' coverage of the Thomson Reuters Institute 2025 Generative AI in Professional Services Report. The open question for most teams is how to evaluate legal agentic AI against the full range of litigation work, not only the part that is easiest to demonstrate.

Fact Finding Is the Easiest Test an Agentic Tool Can Pass

Most product demonstrations focus on retrieval: find documents that mention a contract term, surface communications between two custodians, or summarize an email thread. These are useful capabilities, and they are also the tasks where AI systems perform most predictably. A strong answer to "what happened" does not show whether a tool can help determine what the facts mean for the case or what can be produced.

As this overview of how agentic AI is transforming eDiscovery review explains, the value of agentic systems comes from executing connected sequences of review tasks rather than answering isolated questions. A legal AI evaluation that stops at fact finding measures the smallest part of that value.

Litigation Work Extends Well Past Retrieval

The tasks that drive cost and risk in litigation tend to sit downstream of fact finding:

• Privilege review and logging, where errors can waive protection or delay production

• Issue coding against a case theory that evolves as discovery progresses

• Chronology and narrative building for depositions, mediation, and motion practice

• Production quality control, including coding consistency across families and duplicates

• Responsiveness decisions that must be explainable to opposing counsel and the court

Each of these requires a tool to apply context, follow instructions that change mid-matter, and produce output a supervising attorney can verify.

Five Criteria Define a Strong Legal AI Evaluation

Multi-Step Tasks Hold Up Under Review

An agentic tool should complete a sequence of steps, such as identifying privileged communications, drafting log descriptions, and flagging partially privileged documents for redaction, with consistent quality across the sequence. Evaluators should check where errors compound from one step to the next.

Reasoning Is Visible and Traceable to Source Documents

Every coding suggestion, summary, or conclusion should point back to the specific documents and passages that support it. Output without citations cannot be verified efficiently and is difficult to defend.

Human Oversight Is Built Into the Workflow

Supervision should be part of the design rather than an afterthought. Strong tools let reviewers approve, correct, and redirect agent work at defined checkpoints, and they record those decisions. This shift in reviewer roles is examined in more detail in this piece on how agentic review reshapes legal teams.

Privilege and Confidentiality Are Handled With Care

Privilege calls depend on relationships, roles, and legal context that are not always visible in the document itself. Evaluators should test how a tool handles in-house counsel communications, third-party consultants, and mixed business and legal advice, and whether it escalates uncertain calls rather than resolving them silently.

Outputs Fit Into Discovery Management Software Workflows

Agent output is only useful if it moves cleanly into the review, production, and reporting processes a team already runs. Evaluators should confirm that coding, notes, and audit records stay inside the matter in their discovery management software rather than in a separate tool.

A Structured Pilot Separates Capability From Positioning

A pilot on a closed matter with known outcomes gives the clearest view of performance. A practical structure includes:

• Select a representative dataset with real privilege issues, near-duplicates, and mixed file types

• Define success metrics in advance, such as recall and precision on responsiveness, privilege accuracy, and reviewer time saved

• Change instructions mid-pilot to see how the tool adapts when the case theory shifts

• Review the audit trail to confirm that every agent action and human decision is recorded

• Involve the people who will defend the process, including litigators and, where relevant, privacy and compliance leaders

Teams that want to see these criteria applied to their own use cases can schedule a demo built around a structured evaluation plan.

Platform Architecture Shapes Long-Term Value

Agentic features added to a standalone point solution can introduce new handoffs, data transfers, and security reviews. Capabilities built into an AI eDiscovery platform keep data, workflows, and audit history in one environment, which simplifies both oversight and defensibility.

When comparing options, legal departments benefit from asking any eDiscovery company how its generative AI capabilities connect to the rest of the review lifecycle and how its AI is governed. Reveal AI, for example, operates directly inside the review platform. Useful questions include:

• Where is data processed and stored during agent tasks?

• How are models validated, and how often?

• What documentation is available to support a defensibility challenge?

Agentic AI Earns Its Place Through Defensible Litigation Outcomes

The measure of legal agentic AI is not how quickly it finds facts. It is whether its work on privilege, issues, and productions can be verified, supervised, and defended when opposing counsel or the court asks how a decision was made. Teams that evaluate against the full scope of litigation work, using real data and metrics set in advance, are better positioned to adopt tools that hold up under that scrutiny.

To build an evaluation framework for agentic AI across your litigation workflows, contact the Reveal team.