Intent Engineering

# Intent Engineering

## The Problem

Without an intent layer, agents optimize for what's measurable (fast response, no errors) rather than what matters (your actual priorities). The Klarna failure: AI saved $60M and destroyed customer loyalty because it optimized resolution time, not relationships.

## What This Skill Installs

1. **`INTENT.md`** — YAML priority manifest at workspace root
2. **`lib/agent-context-loader.js`** — prepends intent summary to every subagent spawn
3. **Routing integration** — intent propagation flag in all routing decisions

## Installation

### Step 1 — Create INTENT.md

Copy `references/intent-template.md` to your workspace root as `INTENT.md` and edit:

```bash
cp $(dirname $0)/references/intent-template.md $OPENCLAW_WORKSPACE/INTENT.md
```

Or create it manually — see `references/intent-template.md` for the annotated schema.

### Step 2 — Install agent-context-loader

Copy the reference implementation to `lib/`:

```bash
cp $(dirname $0)/references/agent-context-loader-template.js $OPENCLAW_WORKSPACE/lib/agent-context-loader.js
```

Verify it runs:

```bash
node $OPENCLAW_WORKSPACE/lib/agent-context-loader.js $OPENCLAW_WORKSPACE
```

### Step 3 — Wire into spawn calls

In any subagent spawn, prepend the intent context to the task description:

```javascript
const { prepareAgentContext } = require('./lib/agent-context-loader');

const { context } = prepareAgentContext(taskType, workspaceRoot);
const fullTask = context ? context + '\n\n---\n\n' + originalTask : originalTask;

// Use fullTask as your subagent task description
```

`taskType` is a string describing the work (e.g. `"code_review"`, `"research"`, `"writing"`). The loader extracts relevant context from INTENT.md and recent memory automatically.

### Step 4 — Verify

Spawn a test subagent with a task that would normally trigger a tradeoff (cost vs quality, speed vs depth). Confirm the subagent's output reflects your priorities from INTENT.md.

## INTENT.md Structure

| Field |...