In the previous article, I discussed the idea of tool contracts. The core idea was simple: an AI agent should not have vague, unconstrained access to financial data systems. It should interact with those systems through explicit capabilities. Each capability should have a clear purpose, input structure, output structure, evidence expectation, and policy boundary. For … Continue reading AI-Native Financial Data Foundation (32) – MCP: A Standard Interface for Agentic Financial Data →

In the previous article, I explored a retrieval pattern for the financial semantic layer. The pattern is: user question → classify intent and extract context → scoped hybrid candidate search → entity resolution → constrained graph traversal → governed evidence → LLM explanation For governed financial semantic questions, the LLM should not freely inspect the … Continue reading AI-Native Financial Data Foundation (31) – Tool Contracts: Making Agent Capabilities Explicit →

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 →