让AI调用API出错时自动知道怎么改，提升修复成功率。
Self-Reflective APIs: Structure Beats Verbosity for AI Agent Recovery
- API出错时返回结构化修复建议，无需额外推理。
- 在Anthropic模型上任务完成率提升36.7%至40.0个百分点。
- 适合构建高鲁棒性的AI代理系统，尤其关注API调用可靠性。
当AI代理调用API并遭遇验证错误时，它不仅需要知道哪里出错，更需明确下一步操作。自省式API在验证失败时返回可机器解析的recovery_feedback.suggestions[]，使代理能自主修复请求并重试，无需外部推理。在一次泄漏审计后的试点测试中（每组N=30，3个LLM，10个对抗性任务），结构化建议使任务完成率相比纯英文诊断提升36.7%–40.0个百分点（Fisher精确检验p ≤ 0.0022），且每成功令牌效率提高1.8–2.2倍。该优势在gpt-4o-mini上不显著（p=0.435）；在账单API上的跨领域复现验证了此模式。该对比仅在审计两个未公开的答桉泄露类别后成立。代码与数据已开源：https://github.com/arquicanedo/self-reflective-apis。
原文摘要 · Abstract (English)
When an AI agent calls an API and hits a validation error, it needs more than what went wrong -- it needs what to do next. A self-reflective API returns, on validation failure, a machine-readable recovery\_feedback.suggestions[] payload sufficient for the agent to repair the request and retry without external reasoning. On a leak-audited pilot ($N{=}30$ per cell, 3 LLMs, 10 adversarial tasks), structured suggestions lift task-completion rate by $+36.7$--$40.0$pp over plain-English diagnoses on Anthropic models (Fisher's exact $p \le 0.0022$), at $1.8$--$2.2\times$ better per-success token efficiency. The lift is not significant on gpt-4o-mini ($p{=}0.435$); a second-domain replication on a billing API confirms the pattern. The comparison only holds after auditing two undocumented classes of answer leakage in LLM benchmarks. We shipaudit\_prompt\_leakage.py as reusable CI infrastructure. Code and data: https://github.com/arquicanedo/self-reflective-apis.
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