税务处理团队终于有了靠谱的AI助手——Tax AI解决了K-1等复杂文档的痛点，准确率高达97%且能自我改进，做税务自动化的开发者可以直接参考其产品设计思路。
OpenAI与Thrive合作开发了一款自改进税务代理Tax AI，在30多家会计事务所处理了7000份纳税申报，节省约三分之一准备时间，准确率高达97%，吞吐量提升约50%。该系统的难点不在于处理W-2或1099表格，而在于处理混乱的K-1表格、租赁附表、笔记、电子表格、往年文件以及跨文档必须匹配的值。系统记录完整追踪：源文件、提取字段、引用、税务引擎映射、会计师更正和最终归档值。重复的更正成为评估目标，使Codex获得带有证据、代码、测试和通过条件的窄任务。巧妙之处在于不是简单用Codex编写修复，而是构建了一个产品环境，让从业者的重复更正成为有边界、可测试的工程任务。
OpenAI and Thrive just built a self-improving tax …
OpenAI and Thrive just built a self-improving tax agent with up to 97% accuracy.
Tax AI processed 7,000 returns across 30+ accounting firms, saved about one-third of preparation time, reached up to 97% accuracy, and raised throughput by about 50%.
The hard part was not reading W-2s or 1099s, but handling messy K-1s, rental schedules, notes, spreadsheets, prior-year files, and values that must match across documents.
The system records the full trace: source file, extracted field, citation, tax-engine mapping, accountant correction, and final filed value.
Repeated corrections become eval targets, so Codex gets a narrow task with evidence, code, tests, and a pass condition.
A wrong tax field can come from many places: bad extraction, weak mapping, unsupported workflow, prior-year carryover, or human judgment.
The clever part was not simply using Codex to write fixes, but building a product environment where repeated practitioner corrections became bounded, testable engineering tasks.
In the rental-property example, the agent could inspect source documents, extraction traces, mapper behavior, expected outputs, and regression tests before proposing a change.