Introduction
The adoption of AI tools, and particularly generative AI, has moved fast over the past few years. Adoption levels vary depending on regulatory environments, risk appetite, and organizational maturity, but one thing is clear: AI is now part of how legal work gets done. Whether it is used to generate meeting minutes, reports, or analysis, it is here to stay. As advanced as this technology seems, the question is: what comes next for legal AI? A recent event sponsored by EY and Legaltech Hub pointed out what might be the next big shift: agentic AI.
This shift moves AI from a standalone tool or productivity add on to a fully embedded part of legal processes. Legaltech Hub and EY describe this evolution as a move from “isolated technologies” toward integrated systems that support how legal functions operate within the wider organization.
What could this possibly mean for legal teams?
What is agentic AI
Agentic AI represents a change in how artificial intelligence is designed and deployed. Rather than focusing on individual tasks, agentic AI systems are built to pursue defined goals. They can perceive information, reason through context, select appropriate tools, and take action across multiple systems.
In short:
Gartner defines AI agents as “goal driven systems”, distinct from traditional AI tools or assistants. For legal teams, that distinction matters because legal work is inherently goal oriented. Drafting a contract, reviewing an invoice, or assessing risk are not ends in themselves. They are steps in a wider workflow, supporting outcomes like reducing exposure in a specific situation, accelerating approvals, and enabling the business to move forward with confidence.
In this sense, agentic AI fits the reality of legal workflows far better than task-based automation ever could.
Why legal work lends itself to agentic AI
Unlike a CRM workflow, where processes are typically standardized and predictable, legal work is rarely linear. One can establish workflows, rules, or playbooks to better pinpoint risky clauses, but cases and contracts tend to have meaningful differences between them.
A single matter often spans multiple stages, involves several stakeholders, and touches numerous systems. A contract, for example, moves from request to drafting, negotiation, approval, execution, and ongoing obligation tracking, with each step depending on context, judgment, and timing. This does not mean automation or the use of AI as we know it in the legal realm is useless. It simply means AI needs to operate at a different level, where human input is minimal, teams work by exception, and entire processes can be delegated to an AI agent.
This complexity is exactly why agentic AI is gaining attention in legal. Legaltech Hub and EY note that agents are most effective when work needs to be managed end to end, rather than split into disconnected tasks. They also point out that not all legal work fits naturally into a chat interface. More complex activities require purpose-built user experiences and tight integration with existing enterprise systems.
Considerations around agentic AI for legal teams
Agentic AI sounds promising and exciting, but it also comes with important risks and guardrails.
Control and accountability
Traditional workflows are predictable by design. They are rule based, built to deliver consistent outcomes by following established rules. For example, a legal team may need to track renewals for the marketing team’s contracts. They can establish a workflow that flags upcoming renewal dates based on predefined criteria.
Agents act based on context and autonomous reasoning, which makes their behavior less predictable and their outcomes may variable.
Strong governance foundations
Legaltech Hub and EY emphasize that agents interact with enterprise systems much like human users do. As a result, before deploying AI agents at scale, organizations must ensure that agents have unique identities, that all actions are fully auditable, and that governance frameworks are in place to detect anomalies and manage risk.
Gartner reinforces this perspective, noting that many AI agents today are still at an early stage of enterprise maturity. Their effectiveness depends on strong foundations, including data governance, system integration, and clearly defined decision models.
Without these elements, agentic AI introduces unnecessary exposure rather than value.
Practical applications of agentic AI for legal teams
Contract lifecycle management
One of the most immediate applications of agentic AI is contract lifecycle management. An agent can intake a contract request, identify the appropriate template, route approvals based on predefined rules, initiate signature, and ensure the executed agreement is stored and tracked correctly.
In an ideal setting, beyond eliminating manual coordination, these models reduce administrative overhead because human intervention is minimal.
Legal knowledge management
Knowledge management is another area where agentic AI shows strong potential. Agents can monitor new legal content, extract key insights, generate summaries, apply consistent tagging, and publish information into a centralized knowledge hub.
Legaltech Hub outlines this kind of agent-driven workflow as a way to keep legal knowledge current, accessible, and usable. Done well, it can significantly reduce admin work for teams stretched thin.
Obligation and risk monitoring
Contracts continue to generate obligations long after signature. Agentic AI systems can monitor deadlines, track performance requirements, assess risk exposure, and trigger alerts or workflows when intervention is needed.
This moves legal teams from a reactive posture to a more proactive approach to risk management.
Looking ahead
Agentic AI moves legal teams beyond isolated automation toward an outcome driven system that requires minimal human involvement. This highlights the transformation that’s already begun for the world of legal. Rupali Patel Shah, Head of Legal Solutions at DiliTrust, agrees, “There is a lot of debate as to how pervasive AI will be in the legal profession, but one thing is certain: the future of legal work will be AI-enabled”.
As for Agentic AI, it should be considered an extension of what is already possible with generative AI, and not as a replacement for legal expertise.
It acts as a legal companion capable of reducing admin work even further than generative AI already does, but for it to work, it must be implemented with intention and structure. To achieve real outcomes, legal expertise is still needed, because strong governance and data management are what enable legal AI agents to operate purposefully.
We are still in the early stages of AI agent adoption in enterprises, and this represents an operating model worth keeping in mind for those who want to be one step ahead.
This article was written based on the following sources:
- The current state of AI agents for enterprises, Gartner, 2025 (on demand only)
- EY and Legaltech Hub, The new normal: stabilizing legal change in an AI world
