原标题：Building Legal Agents When You Can't See the Data - Jacob Lauritzen, CTO of Legora
Latent Space 访谈 Legora CTO Jacob Lauritzen，讨论在无法查看客户数据的前提下如何构建和测试法律智能体。团队用三名工程师加三名律师搭建模拟客户工作的测试环境，引用准确率从 70% 升至 99%；目前 100 名工程师基于 Azure 构建，产品覆盖 Word 文档起草、万份合同表格审查和尽职调查，并在纽约、斯德哥尔摩和伦敦招聘。
访谈给出法律智能体在看不到客户数据时如何做测试和评估的具体做法，对同类 Agent 工程有可迁移的参考价值。
本页的摘要、推荐理由与解读出自 AIsa Studio 的 AI 风向智能体，原文版权归原作者所有。
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Legora can’t read a single trace of what its agent does for a lawyer. The data belongs to the law firm’s clients. How do you build and test an agent when you can’t see its work? Jacob Lauritzen is the CTO of Legora, one of the fastest-growing companies...
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Legora can’t read a single trace of what its agent does for a lawyer. The data belongs to the law firm’s clients. How do you build and test an agent when you can’t see its work?
Jacob Lauritzen is the CTO of Legora, one of the fastest-growing companies in legal tech history. The team started in a Stockholm basement with ChatGPT, Swedish law, and a search tool. Today, a lawyer can use Legora to draft and edit Word documents, review 10,000 contracts in a single table, or run due diligence. The agent also coordinates work between lawyers, clients, and other agents. When Legora made its own acquisitions, the team used it for diligence.
What we talked about
• Why Legora resisted adding a model picker for years, and why it finally added one
• Jacob’s estimate that models already do about 60% of legal work well, and why the rest takes a smarter model
• How three engineers and three lawyers build test environments modeled on customers’ work, much like the “worlds” used in reinforcement learning
• A citation problem where the model got it right 70% of the time, then 99% after one release. Legora didn’t have to change anything.
• Why Legora runs everything on Azure and why it doesn’t use ElevenLabs
• Tabular Review, the million-cell spreadsheet the industry copied down to the name
• How intake works, how agents create lists, and how a list can start another agent
• What legal engineers and forward-deployed engineers each do, and who gets to write code
• The 300 Spartans slide Jacob used to argue that 20 engineers would be enough forever. Now there are 100, and he still feels short-handed.
• Why Jacob finds Fable and Opus too chatty for drafting and likes Astra’s concise writing
• Why drafting a long contract is a search problem
• Whether model labs will cut off legal tools the way they cut off coding tools
• What happens to law firm partnerships, the billable hour, and jobs for junior lawyers
Legora is hiring engineers in New York, Stockholm, and London.
Chapters
0:00 Cold open
0:40 Meet Jacob Lauritzen, CTO of Legora
1:05 Starting out as underdogs in a Stockholm basement
1:54 How Legora grew from ChatGPT with Swedish law into a legal agent
4:12 The switch from assistant to agent
5:23 Which models Legora uses and why it added a model picker
7:48 Are today’s models good enough for lawyers?
8:20 What agents couldn’t do nine months ago
9:18 Testing the agent in simulated worlds and a 10,000-file data room
10:07 The evals team and Legora’s 100 legal engineers
11:03 When evals didn’t seem worth it and one release took citations from 70% to 99%
12:18 What legal practice could look like in five to ten years
14:18 Will the law firm partnership model survive?
16:40 What happens to paralegals and junior lawyers?
18:00 The billable hour and why earlier legal tech fell short
19:39 Where the money goes now
22:44 How Legora used its own product for acquisition diligence
23:59 How Swedish and American law firms differ
26:12 Product metrics when you can’t read the traces
27:41 Paraphrasing traces and narrowing failures down to 10 or 20 types
28:42 Why data privacy led Legora to run everything on Azure
30:04 Open-weight models and model arbitrage
30:55 Transcription models and why Legora doesn’t use ElevenLabs
33:33 The agent, Tabular Review, projects, and the Word add-in
34:24 How a lawyer uses Tabular Review
35:51 Why a spreadsheet works and what you learn from the first 10 rows
37:53 How the legal intake bot works in Slack
39:11 How agents create lists and lists start agents
40:15 Building products on those same pieces and acquiring Wexler
42:37 What forward-deployed engineers do at Legora
43:52 How legal engineers and forward-deployed engineers onboard a big client
46:43 What’s next for the forward-deployed team over the coming year
47:24 Why Legora resisted building APIs
49:26 Can the agent be the API?
50:12 The 300 Spartans slide and the plan to stay at 20 engineers
51:32 The bets nobody asked Legora to make
53:05 Post-training open-weight models for legal research
54:05 Search, retrieval, and late interaction
54:51 What makes a good legal agent harness and why drafting depends on search
56:25 Which model writes the best draft?
57:03 Hallucinations and Legora’s citation system
57:41 Do lawyers check the citations?
58:19 MapReduce or one big context window for a million-token contract?
1:00:16 Getting anonymized examples and the idea of buying a law firm for its data
1:01:53 Why Jacob advises against default embeddings
1:02:44 Will lawyers work in Legora or through Claude?
1:03:26 Will the model labs cut Legora off?
1:04:24 Law schools and scholarships for computer science majors
1:05:28 Hiring in New York, Stockholm, and London
1:06:09 Why most work eventually becomes legal work
Guest — Jacob Lauritzen, CTO of Legora
Co-host — swyx, Latent Space
Host — Basil Chatha, Forward Deployed
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Latent Space 于 2026-11-02 发布对 Legora CTO Jacob Lauritzen 的访谈（同日两篇报道）。报道背景：Legora 以 8.8B 估值完成 5500 万美元 Series E（报道写作 $550M Series E），从斯德哥尔摩地下室起步，产品支持 Word 文档起草编辑与一次审查数千乃至万份合同（如 10,000 文件数据室尽调），4 月 ARR 突破 1 亿美元、客户超 1,800 家，年内完成第五次收购（Wexler）。核心内容：由于用户上传内容几乎全部保密且多属律所客户，Legora 无法读取任何 trace，评测团队由 3 名工程师加 3 名律师构建 RL 环境模拟客户工作，引用准确率从 70% 升至 99%；依托约 100 名 legal engineer（另一篇表述为 100 名工程师基于 Azure 构建）池，失败模式归为 10–20 类，基础架构因律所信任原因全部跑在 Azure 上。Jacob 认为模型已能很好处理约 60% 的法律工作，复杂分析和百万 token 合同编辑仍需更强智能；公司正在纽约、斯德哥尔摩和伦敦招聘。两篇报道信息基本一致，仅表述粒度略有差异（如‘100 名工程师基于 Azure 构建’与‘约 100 名 legal engineer 池’）。
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