Mirrors helps engineering teams that run customer-facing AI agents fix mistakes that keep showing up in production. A developer writes a short correction, previews the past conversations it would have reached, and turns it on only for the situations they choose. They can then compare outcomes with and without that correction on real runs. The launch is for teams already maintaining agents in production, where each bad session becomes another line in the system prompt, every request carries all of those lines, and no one can tell which ones still help. Mirrors stores those corrections in one place, scoped to accounts and workflows, and delivers them through an SDK that works with any agent framework. The application keeps control of its model calls and tools. The SDK waits at most 750 ms per user turn, and the agent continues without a correction if Mirrors does not answer in time. A system-prompt line reaches every conversation, and a skill is read only when the model decides it is relevant, so the fix can be missed. Mirrors adds the correction when the situation the team defined comes up, then measures whether the outcome changed. Aryan Singhal and Aditya Gupta are launching the company publicly from Y Combinator's Fall 2026 batch in San Francisco. Singhal, CEO, has published AI/ML research at NeurIPS and EMNLP and previously did ML research at UT Austin and UCSB. Gupta, CTO, studied computer engineering at Purdue. In their YC launch post, they describe a design partner whose agent hit 75 errors editing a single travel guide, even though the rule telling it to stop was already in the prompt four times. The launch video makes the same point in support, with payment, refund, and escalation checks applied at the decision, rather than as another note the agent has to carry on every turn.