Intent Engineering - Make Agents Optimize for What You Actually Want

Adds an intent layer to OpenClaw agents, ensuring subagents optimize for your actual priorities via INTENT.md and context-aware task spawning.

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	Purpose
optimization_priority	Ordered priorities: primary → never_sacrifice
tradeoffs	Explicit rules when objectives conflict
model_tier_intent	Which task types get which model tier
delegation_intent	When to delegate vs handle inline
quality_intent	Per-domain quality standards

See references/intent-template.md for the full annotated template.

How It Works

prepareAgentContext() reads INTENT.md fresh on every call, extracts a compact summary (≤200 chars), and prepends it to the spawn task. Subagents receive:

text

## Intent
Optimize for: user_value > honesty > cost. Never sacrifice: honesty, safety.
Delegate when blocking chat. Prefer depth for architecture/writing.

If INTENT.md is missing, a safe default is injected. The loader never throws — it degrades silently.

References

• references/intent-template.md — Full annotated INTENT.md template with all fields
• references/agent-context-loader-template.js — Complete agent-context-loader.js implementation