TLDR: Agentic AI, an advanced form of artificial intelligence, is set to transform the legal sector by introducing autonomous, goal-oriented, and adaptive capabilities. Moving beyond the content generation of traditional Generative AI, Agentic AI aims to automate complex legal workflows, including case management, contract review, eDiscovery, and billing, thereby enabling legal professionals to concentrate on higher-level strategic tasks. While still emerging, 2025 has seen promising pilots and releases of these tools.
The legal industry is on the cusp of a significant transformation with the advent of Agentic AI, a sophisticated form of artificial intelligence that promises to move beyond the capabilities of current generative AI models. While generative AI has already demonstrated its potential in drafting and refining content, Agentic AI is designed to act as a highly capable, autonomous assistant, proactively adapting to achieve defined objectives with minimal human intervention.
Unlike traditional AI systems that require specific prompts for each task, Agentic AI systems can understand broader objectives and determine the necessary steps to achieve them, as noted by Bob Ambrogi. This new breed of AI is characterized by its autonomy, goal-orientation, and adaptability. For instance, an Agentic AI could automatically notify a lawyer if a new court ruling impacts a case, provide actionable insights, suggest strategic adjustments, or even draft an initial response.
In legal services, Agentic AI is expected to automate a wide array of activities, including case management, legal research, document drafting, and eDiscovery. This shift aims to free attorneys from repetitive, time-consuming tasks, allowing them to focus on more complex, strategic work. According to Gartner’s Emerging Tech Radar 2025, ‘Autonomous Agents’ are identified as an early-stage emerging technology, with several promising pilots, prototypes, and general releases already seen in legal tech during 2025.
Early implementations of Agentic AI in the legal field are focusing on areas such as the triage of incoming matters, AI-augmented client communications, real-time monitoring of litigation dockets, and eDiscovery document review and fact-finding. Tools like Microsoft Copilot, Harvey AI, TackleAI, eDiscovery AI, and Relativity aiR are already demonstrating agent-like capabilities.
Beyond just content creation, Agentic AI, especially when combined with generative AI, has the potential to autonomously manage task workflows, complex content creation, and even decision-making. For example, Agentic AI systems are now capable of managing entire litigation workflows, tracking deadlines, assigning tasks, generating status reports, and following up with clients. In contract management, these tools can identify risks, rewrite clauses, and suggest alternatives, even learning the stylistic preferences of individual firms.
Furthermore, Agentic AI is addressing the tedious task of billing and time tracking. Systems in 2025 can automatically track work across platforms, categorize activities, assign them to clients, and generate detailed, auditable timesheets, flagging anomalies and suggesting adjustments before invoices are sent. This leads to greater transparency for clients and reduced administrative burden for lawyers.
Despite its transformative potential, Agentic AI is not without its challenges and risks. Experts like Aron Ahmadia, Senior Director for Applied Science at Relativity, acknowledge that while Agentic AI offers autonomous and strategic planning abilities, it carries significant risks related to compounding errors and reduced human oversight. Unchecked errors could lead to financial loss, ethical breaches, and regulatory compliance violations in high-stakes legal environments. Additionally, challenges with data quality within law firms need to be addressed for the full deployment of Agentic AI.
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Nevertheless, the consensus is that Agentic AI represents the next evolution in legal AI, promising to automate not just content creation but end-to-end workflows. Legal teams are encouraged to begin experimenting with low-risk, agent-based tasks, such as monitoring court filings or preparing deposition summaries, to prepare for a future with more advanced, autonomous systems. The shift also necessitates a change in legal education, moving towards teaching the design and supervision of intelligent systems rather than just tool usage.
