Workflow Patterns

Multi-Agent Systems: Workflow Patterns 🤖

Imagine you’re the conductor of an orchestra. Each musician (agent) is talented, but the magic happens when they play together in the right order, at the right time.

The Big Picture: What Are Workflow Patterns?

Think of workflow patterns like recipes for teamwork. When you have multiple AI agents (like robot helpers), you need rules for how they work together.

Simple Analogy: Building a sandwich 🥪

• One person gets bread
• Another adds meat
• Someone else adds veggies
• The last person wraps it up

That’s a workflow! Let’s explore 5 ways agents can work together.

1. Sequential Workflows 📝

What Is It?

Agents work one after another, like a relay race. Agent 1 finishes, passes the baton to Agent 2, then Agent 3, and so on.

Real-Life Example

Making a YouTube Video:

1. 🎬 Research Agent → Finds topic ideas
2. ✍️ Script Agent → Writes the script (needs research first!)
3. 🎨 Thumbnail Agent → Creates the image
4. 📤 Upload Agent → Publishes everything

Each step waits for the previous one.

graph TD
    A["Research Agent"] --> B["Script Agent"]
    B --> C["Thumbnail Agent"]
    C --> D["Upload Agent"]

When to Use It?

✅ When Step 2 needs Step 1’s output ✅ When order matters ✅ When you can’t skip ahead

Simple Code Idea

result1 = agent1.run(input)
result2 = agent2.run(result1)
result3 = agent3.run(result2)
final = result3

2. Parallel Workflows ⚡

What Is It?

Agents work at the same time, like a team of chefs each making a different dish. Faster because no waiting!

Real-Life Example

Planning a Birthday Party:

• 🎂 Cake Agent → Orders the cake
• 🎈 Decoration Agent → Buys balloons
• 🎵 Music Agent → Creates playlist
• 📧 Invite Agent → Sends invitations

All happen simultaneously!

graph TD
    START["Party Planning"] --> A["Cake Agent"]
    START --> B["Decoration Agent"]
    START --> C["Music Agent"]
    START --> D["Invite Agent"]
    A --> END["Party Ready!"]
    B --> END
    C --> END
    D --> END

When to Use It?

✅ When tasks are independent ✅ When you need speed ✅ When one task doesn’t need another’s result

Simple Code Idea

# All run at once!
results = parallel_run([
    agent1.run(input),
    agent2.run(input),
    agent3.run(input)
])

3. Conditional Branching 🔀

What Is It?

Agents take different paths based on a condition. Like a “Choose Your Own Adventure” book!

Real-Life Example

Customer Support Bot:

• ❓ Question: Is this about billing?
  • YES → 💳 Billing Agent handles it
  • NO → Is it technical?
    • YES → 🔧 Tech Agent handles it
    • NO → 👋 General Agent handles it

graph TD
    Q["Customer Message"] --> CHECK{About Billing?}
    CHECK -->|Yes| BILL["Billing Agent"]
    CHECK -->|No| CHECK2{Technical Issue?}
    CHECK2 -->|Yes| TECH["Tech Agent"]
    CHECK2 -->|No| GEN["General Agent"]

When to Use It?

✅ When different inputs need different handling ✅ When you need smart routing ✅ When one-size-fits-all doesn’t work

Simple Code Idea

if topic == "billing":
    result = billing_agent.run(msg)
elif topic == "technical":
    result = tech_agent.run(msg)
else:
    result = general_agent.run(msg)

4. Loops and Iterations 🔄

What Is It?

Agents repeat their work until a goal is met. Like practicing piano until you nail the song!

Real-Life Example

Essay Writing with Feedback:

1. ✍️ Writer Agent → Writes draft
2. 🔍 Reviewer Agent → Checks quality
3. Is it good enough?
   • NO → Go back to Step 1 (try again!)
   • YES → Done! 🎉

graph TD
    WRITE["Writer Agent"] --> REVIEW["Reviewer Agent"]
    REVIEW --> CHECK{Good Enough?}
    CHECK -->|No| WRITE
    CHECK -->|Yes| DONE["Final Essay"]

When to Use It?

✅ When quality matters more than speed ✅ When you need self-improvement ✅ When first attempt might not be perfect

Simple Code Idea

while not good_enough:
    draft = writer.run(topic)
    feedback = reviewer.run(draft)
    good_enough = feedback.score > 8

5. Agent Chaining Patterns 🔗

What Is It?

Combining multiple patterns together! Like a recipe that uses boiling, frying, AND baking.

Real-Life Example

Building a Mobile App:

graph TD
    REQ["Requirements Agent"] --> DESIGN["Design Agent"]
    DESIGN --> DEV1["Frontend Agent"]
    DESIGN --> DEV2["Backend Agent"]
    DEV1 --> TEST["Test Agent"]
    DEV2 --> TEST
    TEST --> CHECK{Bugs Found?}
    CHECK -->|Yes| DEV1
    CHECK -->|Yes| DEV2
    CHECK -->|No| DEPLOY["Deploy Agent"]

What’s happening:

1. Sequential: Requirements → Design
2. Parallel: Frontend + Backend work together
3. Loop: Testing repeats if bugs exist
4. Conditional: Deploy only if tests pass

Common Chaining Patterns

Simple Code Idea

# Chain: Sequential + Parallel + Loop
spec = requirements_agent.run(input)
design = design_agent.run(spec)

while True:
    # Parallel
    frontend = frontend_agent.run(design)
    backend = backend_agent.run(design)

    # Merge and test
    result = test_agent.run(frontend, backend)

    if result.passed:
        break  # Exit loop

deploy_agent.run(result)

Quick Comparison 📊

Remember This! 💡

Sequential = One by one, like dominoes 🁡 Parallel = All at once, like fireworks 🎆 Conditional = Choose a path, like a maze 🧩 Loops = Try again, like practice 🎯 Chaining = Mix them all, like cooking 🍳

You Did It! 🎉

You now understand how AI agents work together. These patterns are the building blocks of every smart AI system—from chatbots to self-driving cars!

Next time you see AI doing something amazing, ask yourself: “Which workflow pattern is it using?”

“The whole is greater than the sum of its parts.” — Aristotle That’s multi-agent systems in one sentence!