Self-improving agents need structured feedback and iteration loops
Agents can autonomously improve by turning production use into structured signals that fuel an iteration loop.
To build self-improving agents, you need thoughtfully designed evaluation infrastructure, direct access to practitioners, real-world environments, and frontier agentic capabilities.
Real-world systems often fail in unpredictable ways after deployment. Instead of manual fixes, agents can use production feedback to identify edge cases, adjust prompts, and make durable product improvements autonomously.
Tax AI used practitioner corrections on tax returns to create structured signals. These signals became evaluation targets, which Codex then used to engineer improvements, making the agent measurably better over time.
Self-improving AI agents require a robust feedback mechanism. By converting real-world production data and user corrections into structured signals, agents can autonomously identify and fix issues, leading to continuous enhancement.
This approach is not suitable for systems where feedback is inherently unstructured or where autonomous improvement is not desired or feasible.
- Building self-improving tax agents with Codex ↗OpenAI · OpenAI News · May 27, 2026 · paraphrased
First seen on Subvolts May 27, 2026. Updated Sep 11, 2026. Spotted a problem? Tell us.
- Building self-improving tax agents with CodexOpenAI · OpenAI News · May 27, 2026
The Playbook: reusable how-tos, prompts, configs and tips pulled out of the news.
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Questions people ask
- Which AI tools does this work with?
- Any current model. It was seen with in the sources.
- Where did this come from?
- OpenAI (OpenAI News). Subvolts collected, normalized and credited it; the link above opens the original.