Agentic AI and the Legal Department: Moving from Automation to Autonomy

Artificial intelligence is not new to the legal world. Most law departments are already familiar with traditional forms of AI—natural language processing (NLP), machine learning (ML), and, more recently, generative AI. These technologies have powered everything from document search and contract analytics to e-discovery and chatbot interfaces. NLP enables systems to understand and extract meaning from human language. ML allows them to detect patterns and make predictions based on past data. And generative AI can produce text, code, summaries, and other content in response to user prompts, often with impressive fluency.

But these systems, while powerful, are inherently reactive. They respond to inputs, performing discrete tasks based on specific instructions. Even generative AI, despite its conversational abilities, operates in a loop of question and answer, requiring continuous prompting to complete more complex work.

Agentic AI is different. These systems are not limited to a single task; they can accept an open-ended instruction to solve or complete a multi-step problem or workflow, such as "prepare and check over this investigation report for filing" or "summarize these 20 depositions and create a hyperlinked table of key admissions related to the bank account issue." Then the AI can autonomously gather information from systems to which it has access, plan a multi-step approach to completing the workflow, execute on the plan, surface potential mistakes or limitations, and adapt based on feedback.

The potential benefits are clear: improved service levels, reduced legal spend, enhanced consistency, better reuse of institutional knowledge, and faster decision-making. With proper design, agentic AI can help bring more work in-house, reduce outside counsel dependence for routine tasks, and free up significant time for the in-house team to focus on building critical relationships and strategic work.

State of AI in Legal 2025 reports that 34% of in-house professionals (compared to 17% at law firms) are comfortable with agentic AI. Yet a majority of legal leaders remain understandably cautious. High-stakes workflows add levels of risk in autonomy, data privacy, oversight, and operational alignment. This paper aims to bridge the gap from curiosity to clarity. It offers a pragmatic, legally attuned roadmap: defining agentic AI, mapping where it fits, identifying real risks, showing how to mitigate them, illustrating what can go wrong, and guiding how to build a business case.

Much of the legal department's day-to-day work involves managing complex workflows that span departments, systems, and legal domains. Traditional legal AI tools can support many of these workflows by making individual tasks faster or more accurate, but they rely on humans to coordinate the process. Agentic AI changes that dynamic. It introduces systems that can initiate, manage, and complete legal workflows end-to-end, returning polished outputs rather than piecemeal results. Here is an example:

In a typical scenario, HR emails the legal team asking whether Jordan Smith can be terminated for cause. What follows is a series of distinct, manual steps-some assisted by AI, but none coordinated by it:

• Information Gathering: Legal requests documents from HR—employment contract, disciplinary records, performance reviews, and any prior investigations. HR pulls and sends these manually.
• Document Review and Risk Assessment: Legal counsel uses task-specific tools to analyze the contract, review company policies, and compare the situation to past terminations. An LLM might help summarize the file or generate a rough risk outline, but the lawyer drives each step.
• Drafting and Internal Communication: Counsel writes a legal analysis memo and drafts a plain-language version for HR. AI tools might assist with formatting or grammar, but they don't initiate or shape the response.
• Coordination and Follow-Up: The lawyer sends both documents to HR and the relevant reviewers. Any approvals, questions, or next steps are tracked manually, often over email.
• Documentation and Precedent: If the matter sets a useful precedent, legal must remember to record it. In many cases, there's no structured handoff to knowledge management.

Even with modern tools, each action in this workflow is dependent on people to prompt, guide, and connect the dots. AI helps with pieces, but humans still carry out the process.

With agentic AI in place, the process begins the moment HR submits a request like: "Can we terminate Jordan Smith for cause based on recent performance issues?"

The AI immediately recognizes the nature of the request and treats it as a defined legal workflow. Without waiting for further instruction, it locates Jordan Smith's employment contract, prior performance reviews, disciplinary records, and the applicable sections of the employee handbook. It checks whether a prior investigation was conducted and pulls any related documentation. If HR's summary references a specific policy violation—say, misuse of confidential data—the agent cross-references that policy to determine how it's been applied in similar past cases.

Rather than handing over isolated results, the agent analyzes whether the facts support a for-cause termination under both contractual and legal standards. It considers internal precedent, such as how similar infractions were handled, and flags any inconsistencies or risks. If a required step (like a formal written warning) is missing from the file, the AI suggests remediation options, such as contacting the manager or drafting a supplemental write-up.

The system then generates two tailored outputs: a legal analysis memo for in-house counsel and a plain-language summary for HR that includes the risk level, next-step recommendations, and, where appropriate, language that could be used in the offboarding conversation. Imagine that both documents contain hyperlinked citations so they can be easily verified for review by employment counsel and the HR business partner, with deadlines and follow-ups handled by the agent itself.

Once the matter is resolved, the agent updates the internal legal knowledge base, tags the case for future reference, and recommends whether it should be included in ongoing policy training or compliance tracking.

In this model, the agent takes ownership of the matter, recognizing what needs to happen, gathering and analyzing the relevant inputs, drafting outputs, and keeping the workflow moving. Legal retains oversight and authority, but the coordination, analysis, and drafting are handled proactively. It's not just a faster process. It's a fundamentally different one.