> ## Documentation Index
> Fetch the complete documentation index at: https://docs.10et.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Nightly Loop

> Autonomous overnight improvement cycle

The nightly loop is TENET's autonomous improvement engine. It runs while you sleep, making focused code changes, measuring results, and keeping only what improves.

## How It Works

Every night at 2 AM (configurable):

1. **Mine tuples** — Extract training data from today's journal entries
2. **Synthesize context** — Generate product context summary for agents
3. **Strategic reasoning** — Stratus decides which agents to run based on ROI
4. **Run agents** — 5 rounds per agent, \~1 hour total
5. **Retrain policy head** — If 50+ new tuples since last training
6. **Pick up backlog** — Grab GitHub/Linear issues for autonomous PRs

## Setup

### OpenClaw Cron (Recommended)

If you have OpenClaw running, add a cron job:

```json theme={null}
{
  "name": "nightly-loop",
  "schedule": { "kind": "cron", "expr": "0 2 * * *", "tz": "America/Denver" },
  "payload": {
    "kind": "agentTurn",
    "message": "cd ~/CascadeProjects/my-project && tenet peter daily >> .tenet/nightly.log 2>&1",
    "timeoutSeconds": 3600
  }
}
```

### System Cron

```bash theme={null}
# crontab -e
0 2 * * * cd /path/to/project && tenet peter daily >> .tenet/nightly.log 2>&1
```

### Manual Run

```bash theme={null}
tenet peter daily
```

## What to Expect

First run output:

```
  Peter Parker - Daily RL Agent Loop

  Found 5 RL agent(s):
    • test-coverage (coverage_percent, maximize)
    • code-quality (quality_score, maximize)
    • cli-speed (p90_ms, minimize)
    • telemetry-rl (product_health, maximize)
    • onboarding-success (success_rate, maximize)

  Pre-flight: Mining tuples from journals...
    Mined 12 tuples, 8 new → buffer total: 2772

  Pre-flight: Synthesizing product context...
    ✓ Product context updated

  Layer 3: Strategic reasoning...
    Run: ["test-coverage", "code-quality"]
    Skip: ["cli-speed"] — near optimal
    Reasoning: test-coverage has highest gradient (87% headroom)

  Hub health: ✓ (12ms)

  ── test-coverage ──────────────────────────────
    Baseline: 0.1276
    Round 1: +0.0031 KEPT
    Round 2: -0.0002 REVERTED
    Round 3: +0.0018 KEPT
    Round 4: +0.0012 KEPT
    Round 5: -0.0001 REVERTED

  ── code-quality ───────────────────────────────
    Baseline: 0.6836
    Round 1: +0.0045 KEPT
    ...

  Daily loop complete.
```

## Monitoring

### Check Last Night's Results

```bash theme={null}
# View the log
tail -100 .tenet/nightly.log

# Run the scorecard
bash eval/nightly-scorecard.sh
```

### Scorecard Output

**TENET Nightly Scorecard**

| Agent         | Runs | Kept | Signal? |
| ------------- | ---- | ---- | ------- |
| test-coverage | 15   | 8    | ✓       |
| code-quality  | 10   | 4    | ✓       |
| cli-speed     | 174  | 16   | ✓       |

Keep rate: 28/199 (14.1%)

## Tuning

### Rounds Per Agent

Default is 5. Increase for agents with high gradient:

```toml theme={null}
# .tenet/agents/test-coverage.toml
rounds = 20  # More rounds = more improvement per night
```

### Strategic Reasoning

PP uses recent agent performance to decide who runs. If an agent has plateaued (0 improvements in last 10 runs), it gets skipped automatically.

### Cost Control

Each round costs \~$0.50-1.00 in API tokens (depends on model). 5 agents × 5 rounds = ~$15/night.

Use `--budget` to cap costs:

```bash theme={null}
tenet peter daily --budget 10  # Stop at $10
```