Mirrors is an AI agent staging and testing platform that creates runnable copies of the systems, tools, databases, and services an agent interacts with. It uses traces, agent code, tool code, documentation, or production session data to model system behavior with synthetic data, helping teams test AI agents without relying on live customer or production environments.

The platform supports session replay, regression testing, and side-by-side comparison of agent versions. Teams can replay past interactions against a mirrored environment to identify issues such as duplicate actions, incorrect tool calls, failed workflows, or unintended changes before deployment. Mirrors can be integrated into development and pull request workflows to test prompt, tool, model, and agent updates consistently.

Mirrors is designed for AI applications that use frameworks and SDKs such as LangChain, LangGraph, OpenAI, and Anthropic. Its collector supports Python, TypeScript, and Go, while trace exports and manual instrumentation provide additional integration options. The platform is suitable for developers and engineering teams building customer support agents, workflow automation, tool-using assistants, and other production AI systems that require safe, repeatable testing.

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