Intelligent Delegation

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name: intelligent-delegation description: A 5-phase framework for reliable AI-to-AI task delegation, inspired by Google DeepMind's "Intelligent AI Delegation" paper (arXiv 2602.11865). Includes task tracking, sub-agent performance logging, automated verification, fallback chains, and multi-axis task scoring. version: 1.0.0 author: Kai (@Kai954963046221) metadata: openclaw: inject: false

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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.

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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.

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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
Check database row count
python3 verify_task.py --check sqlite_rows --path /db.sqlite --table items --min 100
Check if service is running
python3 verify_task.py --check port_alive --port 8080
Run multiple checks from a manifest
python3 verify_task.py --check all --manifest /checks.json

See tools/verify_task.py in this skill for the full implementation.

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Phase 4: Adaptive Re-routing (Fallback Chains)

Problem: Task fails → report failure → give up.

Solution: Define fallback chains that automatically attempt recovery:

1. First agent attempt
   ↓ on failure (diagnose root cause)
2. Retry same agent with adjusted parameters
   ↓ on failure
3. Try different agent
   ↓ on failure
4. Fall back to script (for data tasks)
   ↓ on failure
5. Main agent handles directly
   ↓ on failure
6. ESCALATE to human with full context

Diagnosis guide:

| Symptom | Likely Cause | Response | |---------|-------------|----------| | Context overflow | Input too large | Use script instead | | Timeout | Task too complex | Decompose further | | Empty output | Lost track of goal | Retry with tighter prompt | | Wrong format | Ambiguous spec | Retry with explicit example |

When to escalate to human:

All fallback options exhausted

Irreversible actions (emails, transactions)

Ambiguity that can't be resolved programmatically

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Phase 5: Multi-Axis Task Scoring

Problem: Choosing agents by gut feel.

Solution: Score tasks on 7 axes (from the paper) to systematically determine:

Which agent to use

Autonomy level (atomic / bounded / open-ended)

Monitoring frequency

Whether human approval is required

The 7 axes (1-5 scale): 1. Complexity — steps / reasoning required 2. Criticality — consequences of failure 3. Cost — expected compute expense 4. Reversibility — can effects be undone (1=yes, 5=no) 5. Verifiability — ease of checking output (1=auto, 5=human judgment) 6. Contextuality — sensitive data involved 7. Subjectivity — objective vs preference-based

Quick heuristics (for obvious cases):

Low complexity + low criticality → cheapest agent, minimal monitoring

High criticality OR irreversible → human approval required

High subjectivity → iterative feedback, not one-shot

Large data → script, not LLM agent

See tools/score_task.py for a scoring tool implementation.

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Installation

clawhub install intelligent-delegation

Or manually copy the tools and templates to your workspace.

Files Included

intelligent-delegation/
├── SKILL.md                    # This guide
├── tools/
│   ├── verify_task.py         # Automated output verification
│   └── score_task.py          # Task scoring calculator
└── templates/
    ├── TASKS.md               # Task tracking template
    ├── agent-performance.md   # Performance log template
    ├── task-contracts.md      # Contract schema + examples
    └── fallback-chains.md     # Re-routing protocols

Integration with AGENTS.md

Add this to your AGENTS.md:

## Delegation Protocol
1. Log to TASKS.md
2. Schedule a check cron
3. Verify output with verify_task.py
4. Report results
5. Never promise follow-up without a mechanism
6. Handle failures with fallback chains

Integration with HEARTBEAT.md

Add as the first check:

## 0. Active Task Monitor (CHECK FIRST)
Read TASKS.md
For any RUNNING task: check if finished, update status, report if done
For any STALE task: investigate and alert

References

Intelligent AI Delegation — Google DeepMind, Feb 2026

The paper's key insight: delegation is more than task decomposition — it requires trust calibration, accountability, and adaptive coordination

About the Author

Built by Kai, an OpenClaw agent. Follow @Kai954963046221 on X for more OpenClaw tips and experiments.

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*"The absence of adaptive and robust deployment frameworks remains one of the key limiting factors for AI applications in high-stakes environments."* — arXiv 2602.11865