Agentic AI is arriving in legal practice more quickly than many anticipated. Whilst most legal professionals remain familiar with AI tools that generate text, summarise documents, or retrieve information in response to a prompt, agentic AI represents a meaningfully different proposition. These systems do not merely respond, they help move work through a process by surfacing inputs, coordinating actions and suggesting next steps. That distinction is significant and it is reshaping the conversation across the sector.
The central question is no longer simply whether AI can assist with legal tasks, but whether legal teams are prepared to allow AI to play a more active role in how work is sequenced, progressed and monitored.
The most sensible starting point for integrating agentic tools is in routine operational tasks that follow clear rules and carry limited contextual sensitivity. Recent survey data indicates that 52% of legal professionals would trust agentic AI with tasks such as routing requests, organising information, preparing matter updates, flagging missing inputs and managing hand-offs across systems.
Controlled delegation in these areas allows organisations to improve pace and consistency in processes that are often more administratively burdensome than intellectually demanding, whilst simultaneously creating a lower-risk environment in which to test and refine oversight controls.
Once agentic tools have demonstrated reliability in operational tasks, more substantive opportunities begin to emerge. Research summaries, collated materials and suggested next steps can provide meaningful support to legal professionals, particularly in tasks where speed and structure are at a premium. Used thoughtfully, this form of agentic assistance can reduce friction and allow professionals to move through familiar tasks more efficiently.
These use cases are already being implemented by some UK legal teams. Survey findings suggest that 44% of UK legal professionals would be comfortable relying on agentic AI for the first draft of non-binding documents and 40% would use such tools to draft advice or summaries for subsequent review. Where outputs are destined to be client-facing, human checkpoints remain indispensable.
Legal teams should resist the temptation to adopt a single, uniform position on agentic AI. What is required instead is a considered delegation model, one that calibrates the degree of AI autonomy to the level of risk involved in any given task. A significant portion of legal work must remain human-led, both to uphold professional standards and to preserve the distinctly personal character of legal services. Judgment sits at the heart of that category, but it is not the only element.
Tone in sensitive advice, negotiation strategy, the balancing of legal and commercial risk and decisions informed by relationship context or institutional knowledge should remain firmly within the province of lawyers. These are precisely the instances in which professional accountability is most visible. The guiding principle is straightforward: machines handle mechanics; lawyers own judgement.
For legal teams thinking seriously about the governance that autonomous systems require, the practical questions are clear. What can the system access? What actions can it take without prior approval? Where is sign-off required? And what record is created throughout the process?
Deploying agentic AI tools without structured policies, communicated clearly to all staff with access, is not a viable approach. If agentic systems are to play a larger role in legal workflows, governance must be embedded in the model from the outset. That means defined permissions, explicit checkpoints, visible logging and named accountability for decisions and outcomes. Governance is what transforms experimentation into a durable and defensible model.
