Why Legal AI Has to Be Built from the Ground Up - Legal Reader

The article argues that the true value of agentic legal AI lies not in the sophistication of the underlying model but in the strength of the surrounding system that enables autonomous execution. It notes that while earlier AI tools were evaluated on output quality, legal departments now want AI to independently route requests, update systems, manage approvals, and handle exceptions, a transition hampered by legacy platforms built for human operators. New benchmarks reveal a significant gap between AI-generated outputs and reliable workflow execution, with low trust in AI outputs and frequent need for rework. To achieve enterprise-scale autonomy, systems must excel at search and retrieval across fragmented data, orchestrate end-to-end workflows, handle exceptions with transparent governance, and provide explainable decisions. The article provides a practical checklist for evaluating agentic claims, covering autonomous review, workflow execution, retrieval quality at scale, and governance. Ultimately, legal teams that invest in architectures designed for routing, approvals, auditability, and exception handling will gain the most from AI.

(Source：Legal Reader)