Our Take
Agents that reason through multi-step legal tasks are real and in early use, but the story is governance, not capability—firms adopting them now are solving control and risk, not speed.
Why it matters
Legal teams are moving from tools that execute instructions to systems that plan their own execution paths. The shift demands new workflows around data access, integrations, and human verification before broader adoption happens.
Do this week
Legal operations leaders: identify one low-risk, high-repetition task (document checklist verification, data extraction) and pilot an agent on it in the next 30 days so you can build governance muscle before scaling.
AI Agents Enter Legal Practice
Legal AI is crossing a threshold. On Harvey's platform, hundreds of agent use cases are emerging—some simple (checking documents against due diligence checklists), others complex (generating full legal document sets by gathering context and drafting outputs). Unlike traditional software that executes predefined steps, these agents reason about outcomes, plan their own paths to reach them, and execute with a degree of autonomy.
Harvey legal innovation partner Tara Waters and Legal IT Insider editor Caroline Hill discussed the shift in a recent podcast. Waters characterized the move this way: agents can take an outcome like "produce a due diligence report" and determine for themselves how to get there. That's fundamentally different from prior legal automation.
The tools are already in early access and seeing real use. But early adoption is cautious. Lawyers are rightly uncomfortable ceding decision-making to systems they cannot fully predict or control.
Governance Is the Bottleneck, Not Capability
The legal sector's skepticism is not misplaced. When an agent operates independently—gathering context, making choices about what to do next, generating outputs—oversight becomes harder, not easier. Firms adopting these tools now are not buying speed; they are buying a governance problem they must solve first.
Waters and Hill emphasized that the recommended approach mirrors any new technology: start small with low-risk tasks, test thoroughly, and expand only as confidence in outcomes and controls grows. Data access controls, system integrations, and risk allocation must be clear before agents scale.
The human role does not disappear. It shifts. Lawyers design agents, test them, verify outputs, and remain accountable for the work—whether it was generated by a person or a system. Legal judgment becomes more important, not less, because someone must ensure the agent's output meets the required standard.
How to Prepare
Start by mapping tasks that are repetitive, low-stakes, and easy to verify. Document review against a fixed checklist, data extraction from contracts, or preliminary document assembly are candidates. Assign a single owner (lawyer or operations lead) to oversee the agent's work and sign off on output quality.
Next, audit your current data access and system integrations. Agents require access to information and tools to be useful, but that access is also where risk concentrates. Know what your agent can touch and build approval workflows around sensitive decisions or data.
Finally, resist the urge to automate judgment calls first. The tools work best on tasks where the criteria are clear, outcomes are measurable, and mistakes are reversible. Leave high-stakes decisions—strategic advice, novel legal questions, client-facing work—to humans until you have built confidence in your oversight model.