> ## Documentation Index
> Fetch the complete documentation index at: https://runmirrors.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# How it works

> Traces in, a runnable copy of your agent's world out.

Mirrors builds a **mirror** (also called a *twin*) of your agent's production environment from its traces, then lets you replay real sessions against any version of the agent — safely, deterministically, and at CI speed.

## From traces to a runnable world

A mirror is built in three parts from the traces the [collector](/collectors/python) streams (or that you upload):

1. **Schema** — which entities exist in your agent's world and how they connect, inferred from the tool calls and results in your traces.
2. **Seed** — a fabricated dataset that populates the schema. The mirror keeps only the *skeleton* of the data; every actual value is fabricated, so redacted or anonymized sessions work fine.
3. **Tool bindings** — each of your agent's tools is bound to the mirror: database-backed tools hit the seeded database, external APIs are mocked or LLM-simulated, and every binding is scored against the real traces.

The result is an isolated, runnable environment. Your agent runs against it exactly as it would against production — same tools, same shapes of data — but nothing real is touched, so even destructive flows (refunds, deletes, sends) are safe to exercise.

## Fidelity scoring

Every mirror is scored **per tool** against the recorded traces: for each tool, how often does the mirror return responses consistent with what production returned? Fidelity tells you which parts of the mirror you can trust and where to invest [business context](#business-context) to lift it.

## Deterministic seeding

The same seed and instructions produce a **byte-identical world**. That makes replays reproducible: you can rerun the exact session that paged you, on demand, and compare two versions of the agent knowing the environment didn't move underneath them.

## Business context and proposals

Traces don't capture everything — pricing rules, eligibility logic, tone guidelines. You can attach **business context** (free text, from the dashboard, `mirrors context add`, or MCP) that the simulation honors, lifting fidelity. Mirrors can also generate **proposals** — agent-suggested changes to the mirror — which you review and accept to trigger a rebuild.

## Replays and evals

* **Replay** — run a recorded session against a new version of the agent and diff the outcome against the baseline.
* **Evals** — named sets of test cases (auto-generated from your traces or authored by hand) that run against a mirror and score the agent's behavior. Eval sets are what the [CI gate](/ci-gate) runs on every pull request.
* **Playground** — an interactive console in the dashboard for querying and chatting with a mirror.

## The surfaces

Everything above is reachable four ways, with full parity:

| Surface       | Where                                                                                                        |
| ------------- | ------------------------------------------------------------------------------------------------------------ |
| **Dashboard** | [runmirrors.com](https://www.runmirrors.com) — Ingest, Build, Simulation, Replay, Evals, Context, Playground |
| **CLI**       | [`mirrors`](/cli) — `pip install "mirrorkit[cli]"`                                                           |
| **MCP**       | [Hosted server](/mcp-server) at `https://api.runmirrors.com/mcp`                                             |
| **API**       | [Versioned `/v1` REST API](/api-reference/introduction) with workspace keys                                  |