Looking Beyond Generative AI – Agentic AI’s Potential in Legal Services (updated July 2025)
by: Jeff Johnson, Chief Innovation Officer
Generative AI is quickly transforming many industries, including legal services. Many of us are already using (or at least experimenting with) Generative AI, with impressive results. By now, most will have heard, read about, or experienced the next step in AI’s evolution, Agentic AI. Generative AI has demonstrated potential to drive professional efficiency. Agentic AI takes that up a notch … or ten. While Agentic AI tools are still emerging, 2025 has already seen several promising pilots, prototypes, and general releases in legal tech. This post is an introductory comparison of how the two differ, relate, and may be used together to elevate our workflows.
What Is Agentic AI?
Think of Agentic AI as a highly capable, autonomous assistant. This category of AI Agents goes beyond simple automation of repetitive tasks (tracking deadlines, monitoring court filings, sending reminders, reviewing a document, etc.). To a degree, Agentic AI can proactively adapt to achieve its defined objectives, without (or with minimal) human involvement. For example, it could automatically notify you if a new court ruling affects your case, provide actionable insights, suggest possible adjustments to legal strategy, or draft an initial response.
Agentic AI is:
- Autonomous: It doesn’t need constant direction.
- Goal-Oriented: It focuses on completing specific tasks or achieving certain objectives.
- Adaptive: It adjusts to changes, like new filings or case developments.
In legal work, this means further automating activities in case management, research, drafting, and eDiscovery, leaving attorneys free to focus on higher-level strategic activities.
According to Gartner’s Emerging Tech Radar 2025, “Autonomous Agents” are an early-stage emerging technology. In legal workflows, Microsoft Copilot, Harvey AI, TackleAI, eDiscovery AI, and Relativity aiR (as well as many others) already demonstrate agent-like capabilities.
What Is Generative AI?
Now, let’s talk about Generative AI. This AI type is all about creating content. When you give it a prompt—input context and a question or request—it can generate drafts, summaries, and other written content. Imagine you need to draft a lease agreement—Generative AI can produce a detailed first version based on your input, saving you hours of original drafting time.
Generative AI shines in:
- Drafting initial versions of legal documents.
- Summarizing long documents into quick, digestible insights.
- Tailoring content for different audiences, like clients, courts, or colleagues.
- Generating review decision suggestions (responsiveness, privilege, etc.), with rationale
Generative AI is a creative powerhouse that helps you draft and refine content. Combined with the goal-oriented autonomy of Agentic AI, there is great potential to cover a wide array of activities in both procedural and creative areas of legal work.
Agentic AI in Legal Services
Agentic AI, combined with Generative AI capabilities, has the potential to autonomously handle task management, complex content creation, and even decision making.
Early legal implementations focus on:
- Triage of incoming matters
- AI-augmented client communications
- Real-time monitoring of litigation dockets
- eDiscovery document review and fact finding
The theory is that by integrating Generative and Agentic AI, the system reduces the need for “human in the loop” activities traditionally required in many aspects of legal services, freeing up human resources for higher-level strategic activities.
What are Agentic AI’s Limitations?
Agentic AI is often confused with AGI (Artificial General Intelligence). AGI remains theoretical and would require reasoning across domains and tasks, far beyond what today’s systems can do. Agentic AI, by contrast, is domain-specific task automation defined by a clear set of goals and rules.
Its limitations include:
- Lack of general adaptability
- Dependency on well-scoped parameters
- Risks of over-automation in complex legal workflows
What are Agentic AI’s Risks?
Agentic AI solutions, while autonomous and efficient, carry significant risks:
- Compounding errors: Mistakes made early in a workflow can propagate.
- Reduced oversight: Overreliance may lead to ethical or compliance violations.
- Explainability gaps: Auditing decisions become harder as complexity increases.
High-stakes environments like litigation demand robust guardrails. As the ABA noted in Resolution 604 (2024), legal professionals must ensure AI tools align with ethical duties of competence, supervision, and client confidentiality.
For a deeper dive, see Tara S. Emory and Maura R. Grossman, “The Next Generation of AI: Here Come the Agents,” National Law Review, December 2024, summarizing guidance from The Sedona Conference.
How Might Agentic AI Revolutionize eDiscovery?
Generative AI has already altered how we approach eDiscovery. Leading platforms like Relativity aiR, eDiscovery AI, and Everlaw AI Assistant use large language models to assist with responsiveness review, privilege detection, and pii extraction. Yet these tools still depend heavily on upfront human input and validation.
Now imagine Agentic AI systems that can:
- Parse a production request and design an optimized review protocol
- Identify relevant custodians and suggest key date ranges
- Integrate analytics, clustering, and threading to create representative samples
- Execute privilege and responsiveness review in a single pass
- Draft privilege logs and generate strategy summaries with citations to relevant documents and law
This is not as far in the future as you may think. Several vendors and law firms are piloting aspects of this today.
Conclusion
Agentic AI represents the next evolution in legal AI, building on the success of Generative AI. Together, they promise to automate not just content creation, but end-to-end workflows. Legal teams should begin experimenting with low-risk, agent-based tasks—such as monitoring court filings, eDiscovery document reviews, building privilege logs, or preparing deposition summaries. The future will bring more advanced, autonomous systems. Will your team be ready?
Sources Cited:
- NIST AI Risk Management Framework 1.0-https://www.nist.gov/itl/ai-risk-management-framework
- Stanford HAI AI Index 2024- https://hai.stanford.edu/ai-index
- Gartner Emerging Tech Impact Radar 2025-https://www.gartner.com/en/documents/6107027#:~:text=Summary,strategic%20planning%20around%20these%20topics
- National Law Review, December 2024, Emory & Grossman-https://natlawreview.com/article/next-generation-ai-here-come-agents
- ABA Resolution 604 (2024)-https://www.americanbar.org/content/dam/aba/administrative/news/2024/mym-hod-resolutions/604.pdf
