[VIDEO-011] Introduction to Multi-Agent Systems

Track: 3 - Multi-Agent Systems Module: 1 Duration: 10 minutes Level requirement: 31 XP reward: 300 XP

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Scene 1: Welcome to Track 3 (0:00-1:30)

[Visual]: Tool Architect badge transforming into multi-agent constellation [Animation]: Multiple Claude avatars appearing and connecting

[Audio/Script]:

"Congratulations, Tool Architect. You can build powerful tools for Claude.

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But here's a question: What if one Claude isn't enough?

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What if you need:

- A researcher gathering information

- An analyst processing data

- A writer creating content

- A reviewer checking quality

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All working together. Autonomously.

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That's multi-agent systems. And it's the future of AI.

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Welcome to Track 3."

[Lower third]: "Track 3: Multi-Agent Systems | Level 31"

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Scene 2: Why Multiple Agents? (1:30-3:30)

[Visual]: Single agent struggling vs team succeeding [Animation]: Complex task being divided and conquered

[Audio/Script]:

"Single agents have limits:

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Context Window: Even large context fills up

Specialization: One prompt can't optimize for everything

Parallelism: Sequential processing is slow

Reliability: Single point of failure

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Multi-agent systems solve these:

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| Problem | Single Agent | Multi-Agent |

|---------|-------------|-------------|

| Complex task | Struggles | Divides & conquers |

| Long context | Forgets early info | Each agent has clean context |

| Many domains | Jack of all trades | Specialists for each |

| Failure | Everything stops | Others continue |"

[Demo - Use Cases]:

Research Project:
├── Research Agent → Gathers sources
├── Analysis Agent → Processes data
├── Writing Agent → Creates report
└── Review Agent → Checks quality
Code Project:
├── Architect Agent → Designs system
├── Implementation Agent → Writes code
├── Test Agent → Creates tests
└── Review Agent → Code review
Content Creation:
├── Ideation Agent → Generates ideas
├── Draft Agent → Writes content
├── Edit Agent → Refines writing
└── SEO Agent → Optimizes for search

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Scene 3: Agent Architectures (3:30-5:30)

[Visual]: Three architecture diagrams [Animation]: Data flowing through each pattern

[Audio/Script]:

"There are three main patterns for multi-agent systems:

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1. Pipeline (Sequential)"

[Diagram]:

Input → Agent A → Agent B → Agent C → Output
           ↓          ↓          ↓
        Context    Context    Context

[Audio/Script]:

"Each agent processes and passes results to the next. Simple, predictable.

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2. Hub and Spoke (Orchestrated)"

[Diagram]:

                 Agent B
                    ↑
                    │
Agent A ←── Orchestrator ──→ Agent C
                    │
                    ↓
                 Agent D

[Audio/Script]:

"Central orchestrator coordinates specialists. Most flexible.

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3. Mesh (Collaborative)"

[Diagram]:

Agent A ←──→ Agent B
   ↑    ╲  ╱    ↑
   │     ╲╱     │
   │     ╱╲     │
   ↓    ╱  ╲    ↓
Agent C ←──→ Agent D

[Audio/Script]:

"Agents communicate freely. Most powerful, most complex.

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We'll start with Hub and Spoke - the sweet spot of power and simplicity."

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Scene 4: The Orchestrator Pattern (5:30-7:30)

[Visual]: Orchestrator managing workers [Animation]: Tasks being assigned and collected

[Audio/Script]:

"The orchestrator pattern is your Swiss Army knife.

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The orchestrator:

- Receives the main task

- Breaks it into subtasks

- Assigns subtasks to specialist agents

- Collects and synthesizes results"

[Demo - Orchestrator Concept]:

class Orchestrator:
    def __init__(self):
        self.agents = {
            "researcher": ResearchAgent(),
            "analyst": AnalysisAgent(),
            "writer": WriterAgent(),
            "reviewer": ReviewAgent()
        }
    async def process(self, task: str):
        # 1. Analyze the task
        plan = await self.create_plan(task)
        # 2. Execute subtasks
        results = {}
        for step in plan.steps:
            agent = self.agents[step.agent_type]
            results[step.name] = await agent.execute(step.task, results)
        # 3. Synthesize final result
        return await self.synthesize(results)

[Audio/Script]:

"Key insight: Each agent has one job. The orchestrator coordinates.

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This is separation of concerns for AI."

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Scene 5: Agent Communication (7:30-9:00)

[Visual]: Message passing diagram [Animation]: JSON messages flowing between agents

[Audio/Script]:

"Agents need to communicate. How?

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Option 1: Direct Results

Agent A returns data, orchestrator passes to Agent B.

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Option 2: Shared Memory

All agents read/write to common storage.

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Option 3: Message Queue

Agents publish/subscribe to channels."

[Demo - Communication]:

Option 1: Direct (simplest)
research_results = await research_agent.execute(query)
analysis = await analysis_agent.execute(research_results)
Option 2: Shared Memory
class SharedContext:
    def __init__(self):
        self.data = {}
    def set(self, key: str, value: any):
        self.data[key] = value
    def get(self, key: str):
        return self.data.get(key)
context = SharedContext()
await research_agent.execute(query, context)  # Writes to context
await analysis_agent.execute(context)  # Reads from context
Option 3: Message Queue (for complex systems)
We'll cover this in advanced modules

[Audio/Script]:

"Start simple. Direct passing works for most cases.

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Add complexity only when needed."

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Scene 6: Your First Multi-Agent System (9:00-9:45)

[Visual]: Simple two-agent system [Animation]: Task flowing through both agents

[Audio/Script]:

"Let's conceptualize your first multi-agent system:

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Research & Summarize Pipeline

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Agent 1: Researcher

- Takes a topic

- Gathers information from multiple sources

- Returns structured data

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Agent 2: Summarizer

- Takes researcher's data

- Creates concise summary

- Formats for readability

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Two agents. Clear responsibilities. Powerful results."

[Demo Preview]:

User: "Tell me about quantum computing"
→ Researcher Agent:
  - Searches web for quantum computing
  - Finds 5 authoritative sources
  - Extracts key facts
  - Returns structured JSON
→ Summarizer Agent:
  - Receives researcher's JSON
  - Synthesizes information
  - Creates readable summary
  - Adds sections and formatting
← Final output to user

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Scene 7: Challenge Preview (9:45-10:00)

[Visual]: Challenge specification [Animation]: XP reward display

[Audio/Script]:

"Your challenge: Build a Research & Summarize multi-agent system.

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You'll create:

1. A researcher agent that gathers information

2. A summarizer agent that creates reports

3. An orchestrator that coordinates them

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This is your entry into multi-agent AI.

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Complete this for 600 XP and the 'Multi-Agent Beginner' badge.

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Next: We'll dive deep into building the orchestrator."

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Post-Video Challenge

Challenge ID: TRACK3_001_CHALLENGE Type: Code + Terminal Instructions:

Task 1: Create researcher agent

claude "Create a Python class ResearchAgent with:
1. execute(topic: str) method
2. Uses WebSearch to find 3-5 sources
3. Extracts key information from each
4. Returns structured dict with sources and facts"

Validation: ResearchAgent class functional

Task 2: Create summarizer agent

claude "Create a Python class SummarizerAgent with:
1. execute(research_data: dict) method
2. Takes researcher's output
3. Synthesizes into readable summary
4. Includes source citations"

Validation: SummarizerAgent class functional

Task 3: Create simple orchestrator

claude "Create a simple orchestrator that:
1. Takes a topic from user
2. Calls ResearchAgent
3. Passes results to SummarizerAgent
4. Returns final summary"

Validation: Full pipeline works

Task 4: Test the system

claude "Using your multi-agent system, research and summarize:
'The current state of AI in healthcare'"

Validation: Returns coherent summary with sources

Rewards:

• XP: 600 (300 base + 300 challenge)
• Achievement: "Multi-Agent Beginner"

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SEO Metadata

Alt-text: Introduction to multi-agent AI systems - orchestrator pattern, agent communication, pipeline architecture. Learn to coordinate multiple Claude agents for complex tasks.

Tags: multi-agent systems, AI orchestration, agent communication, Claude agents, AI architecture, agent pipeline

Keywords: multi-agent ai tutorial, claude agent orchestration, ai agent communication, multi-agent architecture patterns, agent coordination