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Developer shares three fixes for connecting AI agents to accurate financial data

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A developer building AI assistants for financial use cases documented three critical failures encountered when connecting language models to tax and financial data. The first problem was that raw LLMs answered tax queries from outdated training data without signalling uncertainty, which was resolved by using Model Context Protocol (MCP) to fetch figures from a live tax server at query time. A second issue emerged when tool call failures were silent, allowing the model to improvise answers that looked identical to verified ones; the fix was requiring every returned value to carry a source authority and a verification date. A third problem arose when the AI assistant and a separate web calculator drew from different rate tables and returned conflicting figures for the same salary. The developer's solution was to route both surfaces through a single underlying data engine, eliminating the risk of tables falling out of sync.

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Developer shares three fixes for connecting AI agents to accurate financial data · ShortSingh