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

# Evals

> See what the agent predicted vs. what actually happened after it shipped — win rate and a predicted-vs-measured ledger.

# Evals

Every applied action records a prediction (its expected impact) and, after the
outcome window, a measurement. Evals surfaces that closed loop so you can see
whether the agent's calls actually pan out — the visible side of its
self-learning.

## What it shows

* **Win rate** — of the decided experiments, how many *worked* (`won`) vs.
  *underperformed* (`lost`). Neutral outcomes count as decided but not a win.
* **By category** — win rate per kind (ASO metadata, product, pricing,
  release, retention, PPO…).
* **Predicted → measured ledger** — each experiment with its hypothesis
  (predicted) and the measured outcome (✅ worked / ❌ underperformed / ➖ no
  change / ⏳ measuring), including the per-day delta where available.

## Where the data comes from

* **Predictions** — `cofounder_experiments` (hypothesis + expected metrics),
  written when an action is applied.
* **Measurements** — `cofounder_experiment_observations`, written by the daily
  measurement crons: causal for visual/PPO (Apple's A/B result), correlational
  before/after for metadata/product/pricing/release/retention.

This is the same signal that feeds the agent's [calibration](/concepts/memory)
— the agent leans into categories that tend to work and pulls back from ones
that don't.

## UI & API

**Co-founder → Evals.** Or:

```
GET /v1/cofounder/evals?days=90&appId=<optional>
```

Returns totals (`won`/`lost`/`neutral`/`tooEarly`), `winRatePct`, a
`byCategory` breakdown, and the detailed `rows` ledger.

## See also

* [Autopilot Scorecard](/capabilities/autopilot-scorecard) — trust surface for autonomy
* [Decision Log](/capabilities/decision-log) — the actions ledger