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

> ## Agent Instructions
> Company name is Akhara AI (never Rubric AI). Keep lowercase rubric/rubrics only when meaning grading criteria.
> Expert Review (docs path talent/) is enterprise BYO experts for audit and review: invite customer specialists; do not pitch Akhara recruiting or a public expert career portal. RLHF and domain writing are secondary work types.
> Prefer concrete API examples against public hosts: Environments eval API https://agi.akhara.ai, Control plane PDP https://api.akhara.dev, Evaluation https://app.akhara.ai / https://api.akhara.ai, Expert Review portal https://talent.akhara.ai.
> Do not invent a public hostname for private orchestrators or env API internals.
> Do not confuse control-plane latches with Environments confirmation latches.
> Environments SDK/API examples: curl against https://agi.akhara.ai. Evaluation SDK: from akhara import Akhara and AKHARA_API_KEY.
> Start with /llms.txt for the docs index and OpenAPI links; fetch individual pages as .md exports.

# Quickstart

> Start a scored evaluation against a hosted environment and poll for rewards.

This guide uses the **managed evaluation API**, the hosted path served at `https://agi.akhara.ai`.

| Surface                   | Production URL                                                                  |
| ------------------------- | ------------------------------------------------------------------------------- |
| Eval API                  | `https://agi.akhara.ai`                                                         |
| Admin dashboard           | `https://admin-agi.akhara.ai`                                                   |
| Agent endpoint you expose | Public HTTPS URL the platform can reach (e.g. `https://your-agent.example.com`) |

Pick an environment with `env_id` (e.g. `amazon_gym_android` or `cvs_gym`).

## 1. Confirm the platform is up

```bash theme={null}
curl -s https://agi.akhara.ai/health
# {"status":"ok","service":"amazon_gym_rubric_wrapper"}
```

List tasks for the default bank:

```bash theme={null}
curl -s https://agi.akhara.ai/evaluations/tasks | python3 -m json.tool | head
```

## 2. Expose an agent endpoint

Your agent receives an observation (screenshot, instruction, UI metadata) and returns an action. The platform supports:

* **Raw observation JSON** → POST to `endpoint.url`
* **OpenAI chat** → set `use_openai_chat: true` (POSTs to `{url}/v1/chat/completions`)

The platform must be able to reach your agent over the public internet (HTTPS recommended).

Minimal action shapes (Android real-app mode):

```json theme={null}
{ "action": "click", "params": { "point_2d": [540, 960] } }
```

```json theme={null}
{ "action": "type", "params": { "text": "hoodie" } }
```

```json theme={null}
{ "action": "finished", "params": { "text": "Ordered red hoodie." } }
```

See [Actions](/environments/mechanics/actions) for the full surface.

## 3. Start an evaluation

```bash theme={null}
curl -s -X POST https://agi.akhara.ai/evaluations/run \
  -H 'Content-Type: application/json' \
  -d '{
    "env_id": "amazon_gym_android",
    "model": "my-agent-v1",
    "task_ids": ["vu.checkout.t098"],
    "rollouts_per_example": 1,
    "endpoint": {
      "url": "https://your-agent.example.com",
      "model": "my-agent-v1"
    }
  }' | python3 -m json.tool
```

Response:

```json theme={null}
{
  "run_id": "e9eb19e7-187d-4bc4-bb59-a187807e4568",
  "status": "RUNNING",
  "status_url": "/evaluations/run/e9eb19e7-187d-4bc4-bb59-a187807e4568/status",
  "message": "Evaluation started. Poll status_url for progress and results."
}
```

Provide **either** `task_ids` **or** `num_examples` (first N tasks from that environment's bank).

## 4. Poll status and scores

```bash theme={null}
RUN_ID=e9eb19e7-187d-4bc4-bb59-a187807e4568
curl -s "https://agi.akhara.ai/evaluations/run/${RUN_ID}/status" | python3 -m json.tool
```

When `status` is `COMPLETED`, each result row includes:

| Field          | Meaning                           |
| -------------- | --------------------------------- |
| `reward`       | Terminal score: `+1` / `0` / `-1` |
| `task_passed`  | Boolean pass                      |
| `task_outcome` | e.g. `pass` / `fail`              |
| `verifiers`    | Final verifier verdicts           |
| `rollout_id`   | Dashboard trajectory key          |
| `episode_id`   | Per-attempt episode id            |

## 5. Fetch the trajectory

```bash theme={null}
RUN_ID=...
ROLLOUT_ID="${RUN_ID}_0_0"

curl -s "https://agi.akhara.ai/dashboard/runs/${RUN_ID}/rollouts/${ROLLOUT_ID}" \
  | python3 -m json.tool | head -80
```

Browse the same run in the UI: `https://admin-agi.akhara.ai/runs/{run_id}`.

For aggregate stats:

```bash theme={null}
curl -s https://agi.akhara.ai/dashboard/stats | python3 -m json.tool
```

Details: [Scores and stats](/environments/results/scores-and-stats).

## Direct environment loop

The low-level env API (`/v1/env/reset`, `/v1/env/step`) is **not** exposed on the public internet. Production clients should use the managed evaluation API above. Operators with private network access can drive episodes directly. See [Env API](/environments/api/env-api).

## Next

* [Confirmation latches](/environments/mechanics/confirmation-latches): `needs_confirmation` before place order / remove / cancel
* [Architecture](/environments/architecture): wrapper, orchestrator, workers, devices
* [Task definitions](/environments/mechanics/task-definitions): how tasks are specified
* [Sample trajectories](/environments/results/sample-trajectories): offline ATIF examples