Building self-improving tax agents with Codex

OpenAI, in collaboration with Thrive and Crete, demonstrated a self-improving tax agent built with Codex. According to the OpenAI Blog, the system automates tax filings, enhances accuracy, and accelerates workflows by learning from errors during processing. The agent uses Codex to translate natural language instructions into code, with a feedback loop that allows it to refine its outputs over time. This case study illustrates how AI can handle complex, rule-based tasks in regulated domains like tax preparation, moving beyond simple automation toward adaptive agents. For developers building AI workflows, the key takeaway is the iterative improvement mechanism—agents can be designed to update their own logic based on performance metrics or human verification, reducing the need for constant manual oversight. The project also highlights the importance of integrating domain-specific knowledge and error handling to ensure reliability in production environments.