@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.
View Cached Full Text
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
Similar Articles
Effective context engineering for AI agents
Anthropic publishes a guide defining context engineering as the evolution of prompt engineering, focusing on curating optimal context tokens for AI agents to maintain performance and focus during multi-turn inference.
@eng_khairallah1: https://x.com/eng_khairallah1/status/2053405155630936297
The article argues that context engineering, which involves structuring the information and memory available to an AI, is more critical for performance than prompt engineering alone. It provides a structured overview of a course designed to teach how to build reliable AI systems by managing context layers like session history and persistent memory.
@dair_ai: https://x.com/dair_ai/status/2056018543850754283
A roundup of the top AI papers from May 11-17, covering Lighthouse Attention for long-context pretraining, a comparison of grep vs embedding retrieval for coding agents, and mechanistic interpretability work revealing a geometric calculator in LLMs.
Data readiness for agentic AI in financial services
The article discusses how financial services companies must ensure data quality, security, and accessibility to successfully deploy agentic AI, emphasizing that the technology's effectiveness depends more on robust data foundations than on system sophistication.
After using AI agents for a few months, these are my biggest observations
A personal reflection on the transformative potential of AI agents with persistent memory, arguing that context and workflow organization will become more important than the models themselves.