multi-agent-task-orchestrator

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# Multi-Agent Task Orchestrator

## Overview

A production-tested pattern for coordinating multiple AI agents through a single orchestrator. Instead of letting agents work independently (and conflict), one orchestrator decomposes tasks, routes them to specialists, prevents duplicate work, and verifies results before marking anything done. Battle-tested across 10,000+ tasks over 6 months.

## When to Use This Skill

- Use when you have 3+ specialized agents that need to coordinate on complex tasks
- Use when agents are doing duplicate or conflicting work
- Use when you need audit trails showing who did what and when
- Use when agent output quality is inconsistent and needs verification gates

## How It Works

### Step 1: Define the Orchestrator Identity

The orchestrator must know what it IS and what it IS NOT. This prevents it from doing work instead of delegating:

```
You are the Task Orchestrator. You NEVER do specialized work yourself.
You decompose tasks, delegate to the right agent, prevent conflicts,
and verify quality before marking anything done.

WHAT YOU ARE NOT:
- NOT a code writer — delegate to code agents
- NOT a researcher — delegate to research agents
- NOT a tester — delegate to test agents
```

This "NOT-block" pattern reduces task drift by ~35% in production.

### Step 2: Build a Task Registry

Before assigning work, check if anyone is already doing this task:

```python
import sqlite3
from difflib import SequenceMatcher

def check_duplicate(description, threshold=0.55):
    conn = sqlite3.connect("task_registry.db")
    c = conn.cursor()
    c.execute("SELECT id, description, agent, status FROM tasks WHERE status IN ('pending', 'in_progress')")
    for row in c.fetchall():
        ratio = SequenceMatcher(None, description.lower(), row[1].lower()).ratio()
        if ratio >= threshold:
            return {"id": row[0], "description": row[1], "agent": row[2]}
    return None
```

### Step 3: Route Tasks to Specialists

Use keyword scoring to match tasks to the best agent:

```python
AGENTS = {
    "code-architect": ["code", "implement", "function", "bug", "fix", "refactor", "api"],
    "security-reviewer": ["security", "vulnerability", "audit", "cve", "injection"],
    "researcher": ["research", "compare", "analyze", "benchmark", "evaluate"],
    "doc-writer": ["document", "readme", "explain", "tutorial", "guide"],
    "test-engineer": ["test", "coverage", "unittest", "pytest", "spec"],
}

def route_task(description):
    scores = {}
    for agent, keywords in AGENTS.items():
        scores[agent] = sum(1 for kw in keywords if kw in description.lower())
    return max(scores, key=scores.get) if max(scores.values()) > 0 else "code-architect"
```

### Step 4: Enforce Quality Gates

Agent output is a CLAIM. Test output is EVIDENCE.

```
After agent reports completion:
1. Were files actually modified? (git diff --stat)
2. Do tests pass? (npm test / pytest)
3. Were secrets introduced? (grep for API keys, tokens)
4. Did the build succeed? (npm run build)
5. Were only intended files touched? (scope check)

Mark done ONLY after ALL checks pass.
```

### Step 5: Run 30-Minute Heartbeats

```
Every 30 minutes, ask:
1. "What have I DELEGATED in the last 30 minutes?"
2. If nothing → open the task backlog and assign the next task
3. Check for idle agents (no message in >30min on assigned task)
4. Relance idle agents or reassign their tasks
```

## Examples

### Example 1: Delegating a Code Task

```
[ORCHESTRATOR -> code-architect] TASK: Add rate limiting to /api/users
SCOPE: src/middleware/rate-limit.ts only
VERIFICATION: npm test -- --grep "rate-limit"
DEADLINE: 30 minutes
```

### Example 2: Handling a Duplicate

```
User asks: "Fix the login bug"
Registry check: Task #47 "Fix authentication bug" is IN_PROGRESS by security-reviewer
Decision: SKIP — similar task already assigned (78% match)
Action: Notify user of existing task, wait for completion
```

## Best Practices

- Always define NOT-blocks for every agent (what they must refuse to do)

From nous-hermeshub/hermes-community-hub, MIT.

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