Agentic AI for Legal Work: 4 Practical Use Cases
It’s hard to believe how quickly generative AI has moved from an experimental technology to widespread use in the legal profession over the last two years.
Agentic AI is shaping up to be the next stage. Unlike GenAI, which generates content or analysis in response to a prompt — followed by iteration through more prompts — agentic AI is designed to carry out multi-step tasks, coordinate complex workflows, and take action on a user’s behalf.
These technologies open the doors to genuinely new ways of getting work done, and interest is growing. According to a 2026 Thomson Reuters report, about 18% of in-house legal professionals say their use of agentic AI is already widespread, and another 18% say they're planning to use it.
The outcomes that legal teams want are moving beyond what generative AI alone can do, says Everlaw Senior Solutions Architect Aaron Patton, who helps organizations frame practical AI uses in legal workflows.
“Agentic AI promises a system that can break a task into steps, gather the right information, use the right tools, draft the output, and move the work forward with the right human checkpoints,” Patton says. “That means fewer handoffs, less rework, and faster movement from initial task to usable work product.”
Because of the privacy, security, and other risks associated with agentic AI, legal professionals are thinking of agents more as capable legal assistants than attorneys or decision-makers. And they’re not looking to hand end-to-end legal judgment to autonomous systems. Instead, teams are using agents as copilots for bounded workflows — helping handle the repetitive labor while allowing human expertise to be used where it’s most valuable.
Early use cases are coming into focus across workflows that include litigation, compliance monitoring, legal research, and contract analysis.
1. Agentic AI for Litigation
An early pattern is emerging for use cases in litigation. Agentic AI is being tested as a copilot for document-heavy work and structured workflows, not as a fully autonomous legal operator. Keeping a human in the loop is essential.
Some of the uses legal teams are experimenting with include:
Document synthesis and case knowledge development
This is the strongest early use case. Legal teams want agents that can traverse a large document corpus, identify key facts, build timelines, and return usable work product — chronologies, factual summaries, proof matrices — with citations for attorney review. This maps directly to some of the most labor-intensive work in litigation, and the outputs are highly verifiable.
Review prioritization and issue spotting
Rather than asking agents to replace reviewers, legal teams are more comfortable asking agents to help determine what deserves attention first. Most legal professionals aren’t ready to fully automated review, but using AI to surface the most relevant documents early in a matter is a lower stakes task that still has high value.
It's another case where legal teams will trust an agent to narrow, sort, and spotlight, rather than to make the final call.
Embedded workflow assistance
Legal teams expect agents can help evaluate proposed search terms, suggest better alternatives, assist with production setup, and guide users through litigation operations tasks inside existing platforms. The appeal is in reducing procedural friction.
Connected workflows
AI can also help legal teams move between the tools they already use across a matter. Everlaw’s partnerships with leading AI platforms like Anthropic, Gemini, and Legora offer early examples of this approach.
Everlaw’s Anthropic MCP integration lets users work with Everlaw data in natural language through Claude to search projects, retrieve documents, analyze metadata, create visualizations, and coordinate multi-step workflows.
The Everlaw+Legora partnership connects Legora’s AI-powered drafting environment to evidence and analysis in Everlaw, helping teams prepare witness statements, deposition questions, and briefs grounded in cited evidence.
As AI assistance extends beyond a single platform, it can help connect the evidence, analysis, and drafting while preserving the permissions and safeguards attached to the underlying case materials. Everlaw always remains the governed source for those materials.
Cross-system orchestration
Legal teams can also use a preferred AI interface to trigger multi-step legal workflows across platforms and receive structured results in one place. Consider tools and agents that connect an evidence layer, such as Everlaw, with internal company knowledge and historical documentation, case management platforms, and legal research tools. This combination may allow for a level of orchestration not seen before.
Across all these areas, the constraints are consistent. Legal teams are most concerned about defensibility and auditability. They worry about court and regulatory acceptance. And they want to make sure sensitive data remains inside governed systems.
“Agents may take on pieces of the workflow, but humans still define the task, set the boundaries, evaluate the output, and decide what can be delivered,” Patton says. “For attorneys, the role is to bring legal judgment, context, privilege awareness, and accountability to the process.”
2. Agentic AI for Compliance Monitoring
Compliance monitoring is another area where agentic AI’s capacity for continuous, multi-step work across large sets of document and data holds real promise. Regulatory obligations change, internal policies evolve, and in-house teams are often under-resourced relative to the volume of activity they need to track.
Agents can be designed to monitor for regulatory changes, flag new obligations, surface potential gaps between current practices and updated requirements, and trigger review workflows when relevant changes are detected. The same logic applies to internal compliance: tracking adherence to policy, identifying anomalies, and escalating issues for human review.
Governance, of course, is central. The most credible early compliance applications will be those with clear audit trails and defined escalation points — and a human making the final determination.
3. Agentic AI for Legal Research
Legal teams already use GenAI to speed up research by synthesizing large volumes of data. Agentic AI promises to automate much more of the process.
In traditional legal research, an attorney defines the legal question, runs queries across relevant sources, reviews applicable case law, and synthesizes the analysis in writing. An agent can coordinate this sequence of discrete, manual steps with minimal human input. Given a prompt outlining the goal and parameters, an agent can plan and execute a research sequence across approved sources and return a summary with citations and supporting materials for attorney review.
That human involvement remains essential to catch errors and assure overall quality. But the efficiency gain is significant: Hours of manual searching and synthesis can be compressed into a faster, more structured workflow where the attorney’s time is focused on validation and judgment rather than retrieval.
4. Agentic AI for Contract Review
Contract analysis and due diligence are another natural fit for agentic AI. Human attorneys typically need to analyze hundreds or thousands of contracts to understand obligations, identify potential liabilities, and assess whether key terms could affect a deal — an enormous volume of structured, document-intensive work.
Agentic AI can automate much of this workflow. With attorney oversight, agents can be trained to classify agreements, extract relevant provisions, compare terms against predefined standards, and flag contracts that contain unusual or potentially significant language. The result is a faster, more scalable review process where attorneys focus their attention on the agreements and clauses that matter most, rather than reading through everything manually to find them.
This is a high-value near-term use case precisely because the outputs are reviewable. An attorney can inspect the flagged provisions, check the underlying contracts, and make a judgment call. That preserves human accountability while delivering meaningful efficiency gains.
Building Trust in Agentic AI Through Responsible Use
Legal teams are approaching agentic AI the same way they’ve approached most consequential technology: carefully, with attention to risk, and with a clear preference for starting where they can verify the work.
The Law Society of the UK predicts incremental, rather than sudden and transformational, adoption of agentic AI — for better and worse. Key to success is thoughtfulness: Each little step into agentic workflows must be handled responsibly, rather than as a pattern of on-the-fly shortcuts.
The most credible early use cases for AI agents (document synthesis, review prioritization, and workflow assistance) share a common logic: high volume, human-reviewable outputs, and humans in control of the final decision. That’s where agentic AI is earning trust in legal today.
For legal teams looking to move beyond GenAI into agentic, a thoughtful approach to these early use cases should be the starting point. It will also serve as the foundation for what comes next.
