Building self-improving tax agents with Codex
OpenAI and Thrive describe a Tax AI system where practitioner corrections become structured findings, eval targets, and Codex-scoped fixes.
At a glance
- Source
- OpenAI
- Published
- May 26, 2026
- Read time
- 1 min read
- Primary lane
- Engineering
Quick read
3 bullets- OpenAI and Thrive describe a Tax AI system where practitioner corrections become structured findings, eval targets, and Codex-scoped fixes.
- The system processed 7,000 tax returns, drafts returns with up to 97% accuracy, and improved materially over roughly six weeks.
- The loop depends on production traces, targeted evals, and human review before changes reach customers.
Why it matters
This matters because it makes self-improving agents concrete instead of aspirational. It shows how teams can turn real production feedback into repeatable engineering loops with Codex, rather than relying on slow manual prompt and workflow tuning.
Builder takeaway
OpenAI published this update in the Engineering lane. Use the original source for details, then compare it with related briefings before changing a roadmap, workflow, or production system.
This matters because it makes self-improving agents concrete instead of aspirational. It shows how teams can turn real production feedback into repeatable engineering loops with Codex, rather than relying on slow manual prompt and workflow tuning.
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