Improving agent accuracy with Harvey graph
Harvey Graph organizes legal and matter data, maps how it connects, and hands that structure to agents at run time so they deliver high quality, personalized work.
If you have used an AI tool for legal work, you know the ritual. Before you can ask your question, you explain the situation. You upload the documents. You describe the client, the deal, and how your partner likes things done. Then you ask.
And on the next matter, you do it all again.
That routine is changing, in part because models have become much better at legal task execution. We benchmark new models on agentic legal work in Harvey LAB, and the results show how far their capabilities have advanced. When every agent can reason well, what separates them for legal use cases is what they know before they start: context about the matter, the documents, the precedents, your client preferences, and the way your team works. That context is where agent quality succeeds or fails.
Harvey Graph helps provide that context. It organizes firm data and legal knowledge and maps how they connect, so an agent starts a task the way a well-briefed teammate would: already knowing your preferences, the project, and the precedents that matter to the work.
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