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Multi-Agent Patterns — When Two Agents Beat One

Multi-Agent Patterns

TL;DR: Two specialized agents often outperform one general-purpose agent. The key pattern: a “dispatcher” handles breadth and coordination while a “deep worker” handles depth and learning. This applies across use cases from content creation to code review.

The Core Insight

“Two agents complementing each other beat one agent trying to do everything.”

Instead of building one super-agent, combine:

  • Orchestrator/Dispatcher: Breadth, integrations, coordination
  • Deep Worker: Depth, analysis, learning

This mirrors how human teams work — a coordinator who delegates to specialists.

The Dispatcher + Deep Worker Pattern

┌─────────────────┐
│   User Task     │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│   Dispatcher    │  ← Breadth: routes, coordinates, integrates
│   (OpenClaw,    │
│    Orchestrator)│
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│   Deep Worker   │  ← Depth: analyzes, learns, remembers
│   (Hermes,      │
│    Specialist)  │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│   Result back   │
│   to Dispatcher │
└─────────────────┘

Example Tools

RoleExample ToolsCharacteristics
DispatcherOpenClaw, custom orchestrators20+ integrations, many skills, MCP server/client
Deep WorkerHermes Agent, specialized modelsSelf-learning, isolated sub-agents, memory

Six Practical Implementations

1. Dispatcher + Deep Analysis

Flow: Dispatcher receives task → Deep worker analyzes with parallel sub-agents → Result returns

Use case: Complex research requiring thoroughness

Example:

  • User asks about competitor strategy
  • Dispatcher identifies this needs deep analysis
  • Deep worker spawns sub-agents for: website analysis, social media, news, financials
  • Synthesized report returns through dispatcher

2. Content Pipeline

Flow: Deep worker researches + remembers style → Dispatcher adapts for platforms

Use case: Multi-platform content at scale

Example:

  • Deep worker researches topic, learns from your edits over time
  • Dispatcher takes output and formats for: LinkedIn, Twitter, blog, newsletter
  • Each iteration improves as deep worker accumulates style knowledge

Key insight: The learning loop means less editing over time — the agent learns YOUR voice.

3. Meeting Preparation

Flow: Deep worker analyzes multiple sources in parallel → Dispatcher presents briefing

Use case: Client calls, sales meetings, interviews

Example (1 hour before call):

  • Deep worker parallel-analyzes: company website, LinkedIn profiles, recent news, industry context
  • Dispatcher synthesizes into structured briefing with talking points
  • Ready before you walk into the meeting

4. Website/SEO Monitoring

Flow: Dispatcher collects metrics → Deep worker analyzes trends with historical memory

Use case: Ongoing performance optimization

Example:

  • Dispatcher pulls data from Google Analytics, Search Console, rank trackers
  • Deep worker compares to previous periods it remembers
  • Identifies: “Traffic dropped 15% — similar to pattern from Q2, likely seasonal”

Key insight: Memory of previous periods enables pattern recognition humans miss.

5. Code Review

Flow: Dispatcher interfaces with GitHub → Deep worker reviews with accumulated standards

Use case: Automated quality assurance

Example:

  • Dispatcher watches for PRs via GitHub integration
  • Deep worker reviews architecture, not just syntax
  • Over time, accumulates knowledge of YOUR project’s standards and patterns
  • Reviews become increasingly project-aware

6. Educational Content at Scale

Flow: Deep worker processes source material in parallel → Dispatcher publishes

Use case: Course creation, documentation, knowledge bases

Example:

  • Deep worker extracts key concepts from videos, articles, transcripts
  • Works on multiple pieces simultaneously (parallel sub-agents)
  • Dispatcher formats and publishes to appropriate channels

The Self-Learning Advantage

Some deep worker agents (like Hermes) have automatic self-learning:

Task completed
     │
     ▼
Analyze result
     │
     ▼
Extract reusable pattern
     │
     ▼
Save as skill
     │
     ▼
Apply to future tasks

After each task, the agent:

  1. Reviews what worked
  2. Abstracts the approach
  3. Stores as reusable skill
  4. Applies automatically to similar future tasks

This compounds over time — the agent genuinely gets better at YOUR specific work.

Advanced: Fine-Tuning Your Own Model

After ~1 month of deep worker usage:

  1. Export interaction history (ShareGPT format)
  2. Fine-tune a base model (Llama, Hermes-3)
  3. Result: Personal model that knows your style, preferences, and projects

This is the end-game of self-learning agents — a model that IS your working patterns.

When to Use Multi-Agent vs. Single Agent

ScenarioApproach
Simple Q&A, chatSingle agent
Tasks needing both breadth AND depthMulti-agent
Repetitive tasks that should improve over timeMulti-agent with learning
Multi-platform outputMulti-agent (deep work + distribution)
Integration-heavy workflowsDispatcher + specialists
Cost-sensitive agentic workAdvisor Strategy (cheap executor + expensive advisor)

Implementation Priority

If starting with multi-agent patterns, the author recommends:

  1. Content pipeline — Immediate ROI, saves hours on editing
  2. Competitive research — High value, visible results
  3. Meeting preparation — Impressive, builds trust in system
  4. SEO monitoring — Ongoing value accumulates
  5. Code review — Quality compounds over time
  6. Model fine-tuning — After sufficient data (~1 month)

Connection to Managed Agents

Claude Managed Agents supports multi-agent via the callable_agents feature:

orchestrator = client.beta.agents.create(
    name="Coordinator",
    callable_agents=[
        {"type": "agent", "id": researcher.id},
        {"type": "agent", "id": writer.id},
    ],
)

Limitation: Currently one level of delegation only.

The patterns in this page work regardless of platform — the dispatcher + deep worker model applies whether you’re using Managed Agents, OpenClaw + Hermes, or custom orchestration.

Key Takeaways

  • Two specialized agents often beat one generalist
  • Dispatcher handles breadth; deep worker handles depth
  • Self-learning agents compound improvements over time
  • Start with content pipeline for fastest ROI
  • The patterns are platform-agnostic

Sources