Intelligent Delegation Framework

A practical implementation of concepts from Intelligent AI Delegation (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:

# 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:

#### 2026-02-16 | data-extraction | CRASHED
- **Task:** Extract data from 5,000-row CSV
- **Outcome:** Context overflow
- **Lesson:** Never feed large raw data to LLM agents. Write a script instead.

Phase 3: Task Contracts & Automated Verification

Problem: Vague prompts → unpredictable output → manual checking.

Solution:

1. Define formal contracts before delegating (expected output, success criteria)
2. Run automated checks on completion

Contract schema:

- **Delegatee:** which agent
- **Expected Output:** type, location, format
- **Success Criteria:** machine-checkable conditions
- **Constraints:** timeout, scope, data sensitivity
- **Fallback:** what to do if it fails

Verification tool (tools/verify_task.py):

# Check if output file exists
python3 verify_task.py --check file_exists --path /output/file.json

# Validate JSON structure
python3 verify_task.py --check valid_json --path /output/file.json