In February 2025, we discussed how AI agents would be the next big thing. By now, they are everywhere. In this article, we look at legal AI agents. We answer the following questions: What are legal AI agents? Why do they matter? We discuss their implementation, limitations, and risks. Finally, we look at using SharePoint and Copilot to create legal AI agents.
What are legal AI agents?
Legal AI agents are AI systems designed to perform legal tasks autonomously or semi-autonomously. They combine large language models with tools, memory, and decision-making capabilities to handle legal work. This ranges from routine tasks to complex multi-step legal reasoning.
What are they used for? Legal AI agents can search case law, statutes, and regulations. They summarize legal documents and precedents and identify relevant authorities for a given legal question. They are also capable of drafting contracts, briefs, memos, and pleadings, reviewing and redlining agreements, and extracting key clauses from large document sets. In due diligence contexts, they can scan thousands of documents, flag risks and unusual terms, and organize findings into structured reports. They can also assist with compliance by tracking regulatory changes and checking whether business practices align with applicable rules.
As the above examples show, legal AI agents are different from basic legal AI tools. A simple legal AI tool might just answer a question or summarize a document. An agent goes further. It can break a) a complex legal task into sub-tasks, b) use tools like search engines and databases autonomously, c) iterate on its own output based on intermediate results, and d) operate with minimal human intervention across multi-step workflows.
Why do they matter?
Legal work has historically been expensive, slow, and inaccessible to most people. A straightforward contract review or basic legal question can be expensive when handled by a lawyer. An AI agent can potentially do it in seconds and at a fraction of the cost. This has significant implications for access to justice: millions of people who currently navigate legal situations without any professional help could have meaningful assistance available to them.
For law firms and legal departments, the efficiency gains are substantial. Tasks like due diligence reviews that once required teams of junior associates working for weeks can be compressed into hours. This shifts the value lawyers provide away from time-intensive document processing toward higher-order judgment, strategy, and client relationships.
There is also a competitive aspect. Firms and legal teams that adopt these tools effectively will be able to handle more work, at a lower cost, and with a faster turnaround. This puts pressure on those that don’t to adapt.
Still, the stakes in legal work are high. Errors can cost people their freedom, their money, or their rights. That is why the question of how much autonomy to give these agents, and under what supervision, is one of the more consequential debates happening in the legal industry right now.
Implementation, limitations. and risks
How do legal AI agents get implemented? What are the limitations, and what are the risks? Supervision remains essential.
Implementation: Most legal AI agents come in two forms. The first are standalone platforms that law firms subscribe to. The second are features embedded within existing legal software, such as contract management or e-discovery tools. Implementation typically requires connecting the agent to a firm’s document management systems, legal databases, and research platforms. Larger organizations sometimes go further. They can build custom agents on top of general-purpose models and tailor them to specific practice areas or internal workflows. Data handling is a critical consideration throughout this process. Firms must carefully determine what client information can be fed into these systems while remaining compliant with their confidentiality obligations.
Limitations: The best-known type of limitation is hallucination: AI agents can confidently produce incorrect information, including fabricated case citations that appear entirely plausible.
Beyond that, these systems also struggle with nuanced legal judgment. This is particularly the case in areas that require weighing competing values, predicting how a specific judge might rule, or navigating the unwritten norms of a particular jurisdiction or courtroom.
They also have knowledge cutoff dates, meaning they can miss recent legislative changes or newly decided cases unless connected to live legal databases. (In an earlier article, we discussed how several AIs were not aware that article 1382 of the Belgian Civil Code had been replaced).
Legal AI agents also struggle with complex multi-jurisdictional matters, where the law differs meaningfully across borders. These remain particularly difficult for current systems to handle reliably.
There also are risks that operate on several levels. For individual clients, an over-reliance on AI output without sufficient attorney review could result in bad legal advice. This could have serious consequences. For law firms, there are professional responsibility risks. Lawyers have ethical duties of competence and supervision. These don’t disappear simply because an AI did the work. Regulators are still catching up, and the rules around AI use in legal practice are evolving unevenly across jurisdictions. There is also a broader systemic risk: on the one hand, AI agents make legal services cheaper and faster. But there is a serious risk that the use of AI agents could be concentrated among well-resourced firms and clients. This would then widen rather than close the access to justice gap.
Supervision remains a must. Legal AI agents currently operate across different oversight models. These range from fully supervised (every output reviewed by a human lawyer), to human-in-the-loop (AI acts but a human approves before anything is filed), to fully autonomous (still rare in high-stakes legal work). The general consensus in the legal profession is that human oversight remains essential. This is particularly the case for anything that directly affects a client’s legal rights.
Using SharePoint and Copilot to create legal AI agents
Many law firms are using SharePoint and Copilot. So, how easy is it to set up legal AI agents with SharePoint and Copilot? Let’s find out.
SharePoint serves as the knowledge base for the agent. Law firms can store their documents, standard contracts, internal guidelines, and matter files in SharePoint. And Copilot can then be configured to draw on that repository when answering questions or generating documents. (This was discussed in our article on Retrieval Augmented Generation). The quality of the agent’s output is heavily dependent on how well the SharePoint environment is organized. Poorly structured or inconsistently named documents, e.g., will produce unreliable results.
Through Copilot Studio, law firms can build custom agents that answer questions. The replies are based on internal documents, draft routine correspondence or contracts using firm-approved precedents. The agents can also summarize lengthy agreements and assist with matter intake. They can be deployed within Teams, Outlook, or SharePoint itself. In other words, lawyers can access them within the tools they already use daily.
There are, however, some limitations and risks that must be considered in this context. The main limitation is that this approach works best for internally focused tasks using a firm’s own documents. It is not a replacement for dedicated legal research platforms connected to live case law databases. There are also meaningful governance considerations: firms need to carefully control which documents Copilot can access, since the system does not inherently distinguish between confidential client matter files and general precedents. Microsoft’s data handling and residency commitments also need to be evaluated against a firm’s specific confidentiality obligations before deployment. Are your clients OK with Microsoft accessing their information through Copilot?
Overall, it represents a relatively accessible starting point, but it requires deliberate setup and ongoing oversight to be used responsibly in a legal setting.
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