> ## 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.

# Agent Overview

> Deploy, configure, and manage RL agents that improve your codebase

TENET agents are autonomous workers that make focused code changes, measure the results, and learn from the outcomes. They run on a reinforcement learning loop — keep what improves, revert what doesn't.

## How Agents Work

Each agent has:

* **A metric** — what it's trying to improve (test coverage, startup speed, code quality)
* **An eval script** — how to measure the metric (bash or TypeScript)
* **A scope** — which files it can modify
* **A time budget** — how long each round gets

```
Agent starts
  → Measure baseline (run eval)
  → Make a focused change (via Claude/Sonnet)
  → Measure again (run eval)
  → Score improved? → KEEP (advance branch)
  → Score same/worse? → REVERT (git reset)
  → Write training tuple → policy head learns
  → Repeat for N rounds
```

## Built-in Agents

TENET ships with 5 focused agents out of the box:

| Agent                | Metric            | Direction | What it does                                                        |
| -------------------- | ----------------- | --------- | ------------------------------------------------------------------- |
| `test-coverage`      | coverage\_percent | maximize  | Adds tests for uncovered files. Uses `jest --coverage`.             |
| `code-quality`       | quality\_score    | maximize  | Reduces console.logs, `any` types, TODOs. Adds `@purpose` headers.  |
| `cli-speed`          | p90\_ms           | minimize  | Optimizes CLI startup latency. Lazy-loads, caches, reduces imports. |
| `telemetry-rl`       | product\_health   | maximize  | Improves real user experience: startup speed, session reliability.  |
| `onboarding-success` | success\_rate     | maximize  | Fixes onboarding edge cases in `tenet init`.                        |

## Running an Agent

```bash theme={null}
# List configured agents
tenet peter agent list

# Run a specific agent for 3 rounds
tenet peter agent test-coverage --rounds 3

# Run all agents (swarm mode)
tenet peter agent swarm --rounds 10
```

## Agent Output

```
Running Scoped Agent: test-coverage (3 rounds)

Metric: coverage_percent (maximize)
Time budget: 600s per round
Baseline: 0.1276

── Round 1/3 ──────────────────────────────
Task: Add tests for uncovered files...
✓ Result: 0.1307 (+0.0031) KEPT

── Round 2/3 ──────────────────────────────
Task: Add tests for auth module...
✗ Result: 0.1305 (-0.0002) REVERTED

── Round 3/3 ──────────────────────────────
Task: Add tests for config loader...
✓ Result: 0.1342 (+0.0035) KEPT

Session Complete
  Improved: 2
  Total delta: +0.0066
```

## The Key Insight

<Warning>
  **The eval script is everything.** If the eval measures the right thing, agents improve. If it measures the wrong thing, they waste compute.

  We learned this the hard way — 750 rounds with 2.5% keep rate because eval scripts were at ceiling (test pass rate was already 100%). The fix: eval scripts that measure metrics with real gradient.
</Warning>

Good eval = real gradient (12.8% coverage → 87% room to improve).
Bad eval = ceiling (100% test pass rate → nowhere to go).

## What's Next

<CardGroup cols={2}>
  <Card title="Agent Configuration" icon="sliders" href="/agents/configuration">
    TOML config reference — metrics, scope, time budgets.
  </Card>

  <Card title="Eval Scripts" icon="chart-line" href="/agents/eval-scripts">
    Write eval scripts that produce real gradient.
  </Card>

  <Card title="Peter Parker" icon="spider" href="/agents/peter-parker">
    The meta-orchestrator that runs the nightly loop.
  </Card>

  <Card title="Creating Agents" icon="plus" href="/agents/creating-agents">
    Build custom agents for your own metrics.
  </Card>
</CardGroup>