In the previous article, I walked through several papers on tool-using LLMs and agentic orchestration. The main argument was that an AI-native financial data foundation should not rely on an LLM as a standalone answer generator. The LLM should act as an orchestrator over governed semantic capabilities: concept resolution, field discovery, product decomposition, readiness checking, … Continue reading AI-Native Financial Data Foundation (27) – Reading Notes: Graph-Grounded Reasoning and Evidence Checking →

In the previous article, I shared my reading notes on several survey-type papers to build a broader landscape view of augmented LLMs, GraphRAG, and tool learning. Those papers were useful because they provided context. They showed that modern LLM systems are moving beyond standalone text generation. LLMs are increasingly being augmented with retrieval, tools, external … Continue reading AI-Native Financial Data Foundation (26) — Reading Notes: Tool-Using LLMs and Agentic Orchestration →

Before jumping directly into the agentic semantic layer for the AI-native financial data foundation, I feel I need a bridge. I want to share my reading notes on several key papers that helped me understand this direction and shape the high-level design of the agentic semantic layer. In this post, I will focus on four … Continue reading AI-Native Financial Data Foundation (25) – Reading Notes: Augmented LLMs, Tool Learning and GraphRAG →

At this stage of the blog series, my main focus is still foundational. I am not yet writing about the design and implementation of the AI-native financial data foundation. That will come in the next stage while I am currently developing and testing the proof of concept in parallel. Before moving into architecture, components, implementation … Continue reading AI-Native Financial Data Foundation (24) – “AI-Native”, not “AI-Enabled” →