What Is Agentic AI? What Lawyers and Law Firms Need to Know
Artificial intelligence has moved quickly into legal practice. Lawyers are already using AI to summarize documents, conduct research, review contracts, prepare initial drafts and analyze large volumes of information.
But much of what lawyers have used so far is still based on a relatively simple interaction: the lawyer asks the AI to do something, the AI produces an answer, and the lawyer decides what happens next.
Agentic AI takes that relationship a step further.
Instead of simply responding to an individual prompt, an AI agent can be given a broader objective and work through multiple steps toward completing it. Depending on the system, that may include gathering information, deciding what needs to happen next, using other software, reviewing its own progress and escalating certain decisions to a person.
For law firms, that distinction matters. The question is no longer only whether AI can help a lawyer perform a task faster. Increasingly, the question is whether AI can perform meaningful portions of the workflow surrounding that task.
That creates significant opportunities for legal practice. It also introduces a different set of risks.
What Is Agentic AI?
Agentic AI generally refers to AI systems capable of pursuing an objective through a series of actions rather than simply generating a single response.
The terminology is still developing, and not every product marketed as an "AI agent" has the same capabilities. Some are essentially more sophisticated assistants, while others can plan and execute multiple steps with significantly less human involvement. The American Bar Association has similarly noted that truly agentic systems should have at least some ability to plan and execute tasks toward a defined goal rather than merely respond to prompts.
Consider a basic legal research request.
With generative AI, a lawyer might ask a system to research an issue and prepare a summary. The system generates the response, and the interaction largely ends there.
An agentic workflow could be considerably broader. The system might identify the relevant issues, search approved research sources, retrieve authorities, analyze the results, prepare a memorandum, check citations, compare the analysis against information in the matter file and then route the draft to the lawyer for review.
The objective remains defined by the lawyer. What changes is how much of the process between the instruction and the result can be handled by the system.
That is a meaningful change from many of the applications discussed in How AI Is Actually Used in Legal Practice, where AI primarily assists lawyers with individual components of legal work.
How Is Agentic AI Different From Generative AI?
Generative AI is designed primarily to generate content. A lawyer provides an instruction and the system might produce text, summarize a document, analyze language or answer a question.
Agentic AI introduces action and workflow execution.
The distinction is not necessarily about which system is more intelligent. It is about what the system is allowed and able to do.
A generative AI system might draft an email for a lawyer.
An agentic system could potentially determine that an email needs to be sent, prepare it using information from the matter, route it for approval and, if authorized, send it through another application.
A generative AI system might summarize 50 contracts.
An agentic system could potentially identify which contracts need review, extract specified provisions, compare those provisions against a playbook, flag deviations, prepare a report and send higher-risk agreements to the appropriate attorney.
The more steps the AI can take without waiting for another prompt, the more important the design of the workflow becomes.
How Agentic AI for Lawyers Could Work in Legal Practice
Legal work contains many processes that are more complicated than a single task but still follow a recognizable sequence. That makes certain areas of practice particularly interesting for agentic systems.
Due diligence is an obvious example. An agent could potentially organize documents, identify relevant agreements, extract provisions, compare them against predetermined criteria, flag issues and prepare preliminary findings for attorney review.
Contract workflows present similar possibilities. AI is already capable of assisting with clause identification, comparison and preliminary review, as discussed in Can Lawyers Reliably Use AI for Contract Review? Benefits, Risks, and Limitations. An agentic system could connect those individual capabilities into a broader process involving intake, review, escalation, drafting and approval.
Litigation workflows could also change. A system might organize discovery, identify documents relevant to particular issues, create timelines, prepare summaries and alert lawyers when new information conflicts with an existing case theory.
Other potential uses include regulatory monitoring, matter intake, compliance review, knowledge management and recurring reporting.
The important point is that the value of agentic AI may come less from performing one legal task exceptionally well and more from connecting multiple tasks into a functioning workflow.
The ABA has identified contracts, research, document analysis and other multistep processes as areas where agentic systems are beginning to receive attention, while also noting that adoption remains relatively early.
Why Agentic AI Could Be Important for Law Firms
Law firms have traditionally organized a significant amount of legal work around people moving information from one stage to another.
A document arrives. Someone reviews it. Another person identifies an issue. A lawyer researches the issue. Someone prepares a draft. A more senior lawyer reviews it. The work is revised, approved and delivered.
AI has already begun accelerating individual steps within that process.
Agentic AI could affect the process itself.
That distinction is important because the economics of saving ten minutes on a task are very different from the economics of automating a substantial portion of a recurring workflow.
It could also change how firms think about staffing and training. Some work traditionally assigned to junior lawyers may increasingly be performed first by AI systems, leaving lawyers to review results, resolve exceptions and make higher-level decisions.
That does not eliminate the need for lawyers. It does, however, raise an important question about how lawyers develop judgment if some of the work traditionally used to develop that judgment becomes automated.
The legal profession is already beginning to confront that issue as AI moves beyond isolated productivity tools and further into actual legal workflows.
Agentic AI Also Changes the Risk
The benefits of agentic AI are relatively easy to understand. The risks require more attention.
With a traditional generative AI interaction, an error may remain contained in the output until someone acts on it.
An agent can potentially act on the error itself.
Imagine a system incorrectly classifies a contract provision. If the AI simply presents the classification to a lawyer, the lawyer may catch the mistake during review.
But if that classification determines the next step in an automated workflow, the error may affect which documents are escalated, what analysis is performed, what communication is prepared or what information is entered into another system.
The problem is no longer simply whether the AI produced a wrong answer.
It is how far the wrong answer traveled.
Recent ABA guidance makes this same distinction: as AI moves from generating suggestions to taking actions, supervision, confidentiality and professional responsibility concerns become more significant.
This is why the risks discussed in Risks of Using AI in Legal Work: What Lawyers and Law Firms Should Know become even more important when AI is given greater autonomy.
Human Review Becomes More Important, Not Less
One of the easiest mistakes to make with agentic AI is assuming that greater automation should mean less supervision.
For legal work, the opposite may often be true.
The appropriate level of oversight should depend on what the system is doing and what happens if it is wrong.
An AI system organizing internal documents presents a different risk from one communicating with a client. A system preparing a preliminary contract summary presents a different risk from one approving contractual language. An agent monitoring a regulatory database presents a different risk from one making decisions that affect a client's legal rights.
Law firms therefore need to determine where human approval is required before deploying an agentic workflow.
Some actions may be appropriate for the AI to complete independently. Others may require attorney review. Higher-risk decisions may need to be prohibited entirely.
The important part is deciding those boundaries before the system is operating in live legal work.
Law Firms Need to Know What the Agent Actually Did
Agentic systems also make traceability increasingly important.
If an AI system completes several steps before producing a final result, reviewing only that result may not tell the lawyer enough.
What sources did the system use?
What decisions did it make along the way?
What information did it rely upon?
Did it encounter conflicting information?
Which actions did it take automatically?
Where did a human intervene?
Those questions become particularly important when something goes wrong.
Lawyers have already seen the consequences of relying on inaccurate AI-generated information in court filings. I discussed several examples in AI Hallucinations in Court: What Lawyers Can Learn from Recent Sanctions and Citation Errors.
Agentic AI expands the concern beyond verifying the final text. Firms may increasingly need records that allow them to reconstruct how the system reached a result and what it did afterward.
Monitoring and logging are therefore not merely technical features. In certain legal workflows, they may become essential parts of responsible AI governance. The ABA has highlighted limited monitoring, transparency and stop controls as particular concerns with agent-based systems.
Confidentiality Still Applies
Giving an AI system more autonomy does not change a lawyer's confidentiality obligations.
If anything, an agent that interacts with multiple systems may create additional questions about where client information travels.
A legal AI agent might interact with a document management platform, email system, research database, contract repository or other internal software. Each connection can potentially involve sensitive information.
Before deploying these systems, firms should understand what information the AI can access, where that information is processed, whether it is retained, which third parties may receive it and whether the information can be used for model training.
These concerns are extensions of the confidentiality issues law firms already face with generative AI. They simply become more complicated as systems gain access to additional data and tools.
How Law Firms Should Evaluate Agentic AI
The evaluation process for an agentic system should go beyond asking whether the underlying model produces good answers.
As I discussed in How Law Firms Should Evaluate AI Tools Before Buying Them, firms should evaluate AI against the actual work they expect it to perform.
With agents, that means testing the entire workflow.
Can the system reliably determine the next step? Does it know when to escalate? What happens when information is missing? Can a lawyer stop the workflow? Are actions logged? Does the system remain within the permissions it has been given? What happens when one step fails?
Those questions matter because a workflow can fail even when the underlying AI model performs well.
A system that is accurate 95 percent of the time on an isolated task may behave differently when several dependent tasks are connected. An error early in the process can affect everything that follows.
For law firms, the relevant unit of evaluation may increasingly become the workflow rather than the individual AI response.
Agentic AI Will Require Better AI Training
Agentic AI also changes what AI competence looks like inside a law firm.
Basic prompt training will not be enough.
Lawyers working with these systems need to understand what the AI has authority to do, where human approval is required, how to review its work and what to do when the system behaves unexpectedly.
That training cannot be limited to lawyers either. Legal operations teams, IT professionals, knowledge management professionals and others involved in designing or supervising AI workflows may all play a role.
The firms that benefit most from agentic AI will probably not be the firms that simply give the technology the most autonomy.
They will be the firms that understand where autonomy is useful and where it is not.
What Happens Next?
Agentic AI is still early in legal practice. Many products described as agents today remain heavily supervised, and the capabilities of different systems vary significantly.
But the direction is becoming clearer.
Legal AI is moving from tools that help lawyers complete individual tasks toward systems capable of participating in broader workflows. The ABA has described agentic systems as an emerging phase in which AI can plan, execute and monitor multistep processes while lawyers increasingly serve as supervisors, reviewers and strategists.
That could make legal work faster and allow lawyers to spend more time on judgment, strategy, negotiation and client relationships.
It also means law firms will need to think much more carefully about supervision, authority and accountability.
The important question may no longer be whether an AI system can complete legal work.
It may be how much work we are prepared to let it complete before a lawyer needs to step in.
Frequently Asked Questions
What is agentic AI?
Agentic AI generally refers to AI systems that can pursue an objective through multiple steps, including planning and executing actions, rather than only producing a response to an individual prompt. The degree of autonomy varies significantly between systems.
How is agentic AI different from generative AI?
Generative AI primarily creates content in response to instructions. Agentic AI can potentially use generative AI capabilities as part of a larger process in which the system determines and executes additional steps toward a defined objective.
How can law firms use agentic AI?
Potential applications include contract workflows, due diligence, legal research, document analysis, regulatory monitoring, knowledge management and other repeatable multistep legal processes.
Can agentic AI replace lawyers?
Current agentic systems still require human oversight, particularly where legal judgment, client advice, strategy or consequential decisions are involved. Lawyers also remain responsible for complying with their professional obligations when using AI.
What are the risks of agentic AI for lawyers?
Risks can include inaccurate outputs, errors propagating through workflows, confidentiality problems, inadequate supervision, poor traceability, inappropriate actions and uncertainty over when human intervention is required.
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Risks of Using AI in Legal Work: What Lawyers and Law Firms Should Know
AI Hallucinations in Court: What Lawyers Can Learn from Recent Sanctions and Citation Errors --------------------------------------
About the Author
Cory D. Raines ("Cory Raines") is a Legal AI Consultant and Founder of Raines Legal Group, and PROTIPPZ, where he focuses on legal strategy, emerging technology, AI workflows, and the evolving intersection of law and artificial intelligence.
Posted by Cory D. Raines
The content on this website and blog is provided for general informational and educational purposes only and should not be construed as legal advice. Nothing on this site creates, or is intended to create, an attorney-client relationship.
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