How Agentic Search Unlocks Legal Research Intelligence at Harvey
Traditional retrieval systems reduce legal research to a single query-and-retrieve operation, failing to handle the iterative, multi-source reasoning that complex legal work demands.
The initial agentic prototype had three failure modes: the model queried only one or two sources when broader coverage was needed, treated nearly every query the same regardless of complexity, and the citation system was built and benchmarked only for single-source queries.
Harvey's privacy-preserving evaluation approach improved tool selection precision from near zero to 0.8-0.9, and complex queries now appropriately scale to 3-10 retrieval operations instead of a single tool call.
Frequently asked questions
What did this team achieve with this AI workflow?
Harvey's privacy-preserving evaluation approach improved tool selection precision from near zero to 0.8-0.9, and complex queries now appropriately scale to 3-10 retrieval operations instead of a single tool call.
What tools did this team use?
Harvey, Vault, LexisNexis, iManage, OpenAI Agent SDK, LangSmith.
What results were reported?
Tool selection precision: 0.8-0.9; Retrieval operations for complex queries: 3-10; Legal knowledge sources: 150+ (source-reported, not independently verified).
What failed first in this deployment?
The initial agentic prototype had three failure modes: the model queried only one or two sources when broader coverage was needed, treated nearly every query the same regardless of complexity, and the citation system…
How is this legal document review AI workflow structured?
Query Understanding and Planning → Dynamic Tool Selection and Retrieval → Reasoning and Synthesis → Completeness Check → Citation-Backed Response → Expert Evaluation Feedback Loop.