intelligent-delegation

# Intelligent Delegation Framework

A practical implementation of concepts from [Intelligent AI Delegation](https://arxiv.org/abs/2602.11865) (Google DeepMind, Feb 2026) for OpenClaw agents.

## The Problem

When AI agents delegate tasks to sub-agents, common failure modes include:
- **Lost tasks** — background work completes silently, no follow-up
- **Blind trust** — passing through sub-agent output without verification
- **No learning** — repeating the same delegation mistakes
- **Brittle failure** — one error kills the whole workflow
- **Gut-feel routing** — no systematic way to choose which agent handles what

## The Solution: 5 Phases

### Phase 1: Task Tracking & Scheduled Checks

**Problem:** "I'll ping you when it's done" → never happens.

**Solution:** 
1. Create a `TASKS.md` file to log all background work
2. For every background task, schedule a one-shot cron job to check on completion
3. Update your `HEARTBEAT.md` to check `TASKS.md` first

**TASKS.md template:**
```markdown
# Active Tasks

### [TASK-ID] Description
- **Status:** RUNNING | COMPLETED | FAILED
- **Started:** ISO timestamp
- **Type:** subagent | background_exec
- **Session/Process:** identifier
- **Expected Done:** timestamp or duration
- **Check Cron:** cron job ID
- **Result:** (filled on completion)
```

**Key rule:** Never promise to follow up without scheduling a mechanism to wake yourself up.

---

### Phase 2: Sub-Agent Performance Tracking

**Problem:** No memory of which agents succeed or fail at which tasks.

**Solution:** Create `memory/agent-performance.md` to track:
- Success rate per agent
- Quality scores (1-5) per task
- Known failure modes
- "Best for" / "Avoid for" heuristics

**After every delegation:**
1. Log the outcome (success/partial/failed/crashed)
2. Note runtime and token cost
3. Record lessons learned

**Before every delegation:**
1. Check if this agent has failed on similar tasks
2. Consult the "decision heuristics" section

Example entry:
```markdown
#### 2026-...