Harvey AI Scales Legal Knowledge 10x With Autonomous Agent Pipeline

Authorized AI firm Harvey has constructed an autonomous pipeline that expanded its jurisdictional protection from six to over 60 international locations since August 2025, demonstrating how AI brokers are shifting from experimental instruments to production-grade infrastructure in enterprise settings.

The corporate’s “Knowledge Manufacturing facility” system now ingests greater than 400 authorized knowledge sources—up from 20—utilizing a multi-agent structure that discovers, validates, and deploys new authorized databases with minimal human intervention.

How the Pipeline Truly Works

Harvey’s strategy breaks down into three core elements. A Sourcing Agent maps authorized infrastructure throughout jurisdictions, figuring out authorities portals, court docket databases, and regulatory repositories whereas flagging protection gaps. A Authorized Evaluation Agent then pre-analyzes phrases of service, copyright restrictions, and entry insurance policies, producing structured summaries for human attorneys.

The effectivity features are concrete: attorneys now overview two to 4 sources per hour, double their earlier throughput. That issues once you’re attempting to cowl 60+ international locations.

Fairly than spinning up separate brokers for every jurisdiction—which loses dialog context throughout handoffs—Harvey treats regional sources as parameterized instruments inside a single reasoning system. An lawyer can transfer between Austrian court docket selections and Brazilian statutes in the identical dialog with out the agent dropping monitor of the dialogue.

The Analysis Downside

Giving an agent entry to authoritative sources would not assure it will purpose appropriately. Harvey’s answer consumes roughly 150,000 tokens per supply analysis by means of a four-step course of.

First, the system generates “answer-first” situations—reverse-engineering particular reality patterns from precise authorized supplies that drive brokers to seek out and interpret actual paperwork. Generic queries let fashions reply from coaching knowledge with out citations, which defeats the aim.

Then comes manufacturing simulation, hint validation checking whether or not brokers truly reached the precise content material, and a multi-agent high quality evaluation scoring quotation accuracy, authorized reasoning high quality, and presentation readability on 1-5 scales. A Resolution Agent makes remaining cross/fail calls, routing ambiguous circumstances to human overview.

Why This Issues Past Authorized

The timing aligns with broader enterprise AI tendencies. A December 2025 DeepL survey discovered 69% of world executives predict AI brokers will reshape enterprise operations this 12 months. But the hole between experimentation and deployment stays extensive—trade knowledge suggests solely 23% of organizations efficiently scale brokers throughout their enterprise, at the same time as 39% report energetic experiments.

Harvey’s structure addresses a core problem: treating brokers as “digital staff” requiring governance and oversight moderately than autonomous black containers. Human attorneys nonetheless overview each supply earlier than deployment. The brokers speed up the work; they do not exchange the judgment.

The corporate says it is constructing towards practice-area group subsequent—grouping sources by case regulation, tax codes, and regulatory filings moderately than simply geography. That will let brokers pull from tax authority steerage throughout three jurisdictions concurrently for a single switch pricing query.

For enterprise AI adoption broadly, Harvey’s pipeline presents a template: heavy compute for analysis, strict human oversight at determination factors, and declarative configurations that allow enhancements circulate throughout all jurisdictions without delay.

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