Aryan Singhal
Founder/CEOCo-Founder & CEO @ Mirrors (YC F26). Published 6 AI/ML research papers in high school at top AI conferences such as NeurIPS and EMNLP. Prev. ML research @ UT Austin, UCSB.
Browser storage is unavailable. Keep this page open and export your collection before leaving.

Production AI agents keep making the same mistakes. The usual fix is another line in the system prompt. Over time, the agent carries instructions for dozens of situations, even when it's doing something simple, and every correction becomes another thing to maintain.
Mirrors helps developers maintain production agents by turning recurring mistakes into targeted corrections. Mirrors surfaces recurring mistakes across your production runs. You write a correction, choose the situations where it applies, and preview which past conversations it would have reached. Once activated, Mirrors adds it to your agent only in those situations. You can then compare outcomes with and without the correction to see whether it helped. Unlike skills, the agent doesn't decide what to load. The scope you set does.
Corrections live in one place, scoped to the right accounts and workflows. Mirrors connects through an SDK and works with any agent framework, and your application keeps control of its model calls and tools.
We're building Mirrors for engineering teams running customer-facing agents.
Co-Founder & CEO @ Mirrors (YC F26). Published 6 AI/ML research papers in high school at top AI conferences such as NeurIPS and EMNLP. Prev. ML research @ UT Austin, UCSB.
Founded 2026 · Source
Source-based assessment · automated evidence rules
The retained text does not establish product availability or customer use.
Maturity is an evidence-based assessment, not a success rating. It may understate the current stage where evidence is limited. Coverage records documented areas, not a complete or independently audited company history.