The staging environment
your agents never had.

You check it against your own traffic. The Replay screen diffs each recorded production call against its counterpart in the environment, so you can see where the two agree and where they drift before you trust it with a merge gate. Tools rebuilt from real code behave exactly like the real thing; the ones inferred from traces are the ones to read closely.

The environment runs on invented data. Mirrors learns the shape and behavior of your systems from your traces, then fabricates the values it runs against. Replays hit plausible fake records, so a risky prompt or a shared report never exposes a real customer.

The collector is two lines of code and instruments LangChain, LangGraph, and the OpenAI and Anthropic SDKs automatically. It ships for Python, TypeScript, and Go, and anything else logs traces through one manual call. Evals and the playground attach to your agent script directly. LangGraph works today and more runners are coming.

They watch and score what your agent already did, in production. Mirrors gives your agent somewhere else to do it: a runnable copy of the systems it calls, including the internal ones nobody will hand you a test instance of. They tell you a refund went wrong. Mirrors is where the wrong refund happens instead. Most teams run both.

That is the case Mirrors is built for. It learns each tool from whatever you have (traces, code, or docs), then rebuilds it as a runnable service with its own schema and seed data. No vendor test instance, no staging licence, no ticket to another team, and no production traces required to start. Your agent calls it exactly the way it calls the real thing.