@jerryjliu0: Many AI agents in finance rely on extremely high quality context engineering from documents They can be roughly divided…
Summary
Jerry Liu discusses how AI agents in finance rely on high-quality context engineering from documents, covering use cases like invoice processing and equity research, and shares workshop slides and a repository for building document parsing pipelines with human-in-the-loop review.
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Cached at: 05/17/26, 01:26 AM
Many AI agents in finance rely on extremely high quality context engineering from documents
They can be roughly divided into two categories: Repetitive, operational work common in back-office use cases - invoice processing, loan origination, KYC Assistive agents for open-ended research and generation of reports/presentations - e.g. diligence, equity research
We gave a workshop last week in NYC on how to build a high-quality document context layer to enable these AI agent use cases. At this stage, you need a rigorous OCR layer, evaluation checks, and good UI/UX for HITL review/audit - even a slight mistake in number can have catastrophic consequences downstream.
Check out the resources below: My slides: talk a lot about document processing and the general landscape of knowledge work: https://figma.com/slides/QUUMQqhCsmV6tz8s5Iq9Iu… Logan’s repo on building an agentic document parsing pipeline over financial documents, with full HITL review: https://github.com/logan-markewich/finparse-pipeline…
Our core mission is extracting the highest-quality document context for AI agents in finance and more. Come talk to us if you’re facing relevant challenges: https://llamaindex.ai/contact
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