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9.6.6 AutoGen【Elective】

Section Overview

If some frameworks feel more like a “workflow graph” or a “knowledge organization layer,” then the first impression of AutoGen is usually:

Multiple Agents collaborate to complete a task through round after round of message-based conversation.

Its key idea is not simply “many roles,” but the fact that conversation drives task progress.

Learning Objectives​

  • Understand why AutoGen-style systems emphasize multi-Agent conversation
  • Distinguish its core differences from frameworks like LangGraph / CrewAI
  • Read a minimal AutoGen-style message loop
  • Know when this kind of framework is especially suitable, and when it can get out of control

What is the core intuition behind the AutoGen style?​

It does not start by drawing a flowchart; it starts by letting the roles “talk”​

Many frameworks first ask:

  • What is the current state?
  • Which node should we go to next?

The AutoGen style is more like asking:

  • How should the planner assign work to the coder?
  • After the coder finishes, how should the result be handed to the reviewer?
  • How does the reviewer’s feedback drive the next round?

In other words, it abstracts the system as:

A group of roles that send messages to one another.

A real-life analogy​

You can think of an AutoGen-style system as a group chat for work:

  • The product manager states the requirements
  • The developer takes the task
  • The reviewer gives feedback
  • Everyone keeps discussing back and forth

This analogy is very important because it directly determines the kinds of tasks this framework is good at.

Why does this “conversational multi-Agent” style feel so natural?​

Because many complex tasks already have this shape:

  • State the requirements first
  • Then try to execute
  • Then refine based on feedback

For example:

  • Code generation and review
  • Research report writing
  • Troubleshooting and debugging

These tasks are not really a straight line; they are more like multiple rounds of back-and-forth. So the AutoGen-style abstraction is very close to human collaboration intuition.

A minimal AutoGen-style example​

First, let’s not use a real framework. Instead, let’s use pure Python to get a feel for “multi-round conversational collaboration.”

messages = []

def send(sender, receiver, content):
    msg = {
        "from": sender,
        "to": receiver,
        "content": content
    }
    messages.append(msg)
    return msg

send("planner", "coder", "Please implement a function that checks refund eligibility.")
send("coder", "reviewer", "I’ve written the first version. Please review it.")
send("reviewer", "coder", "Please add logic for cases where learning progress exceeds 20%.")

for msg in messages:
    print(msg)

Expected output:

{'from': 'planner', 'to': 'coder', 'content': 'Please implement a function that checks refund eligibility.'}
{'from': 'coder', 'to': 'reviewer', 'content': 'I’ve written the first version. Please review it.'}
{'from': 'reviewer', 'to': 'coder', 'content': 'Please add logic for cases where learning progress exceeds 20%.'}

Although this code is simple, what is it teaching?​

It teaches you that:

  • The unit of collaboration is a “message”
  • System progress depends on “who said what to whom”
  • Multi-Agent systems do not necessarily need an explicit state graph first

This is the core entry point of the AutoGen style.

Why is AutoGen often tied to “code execution” scenarios?​

Because these scenarios naturally fit multi-round feedback​

Code tasks are rarely finished in one round:

  1. Write the code
  2. Run it
  3. Check the error
  4. Modify it

This matches AutoGen’s message back-and-forth pattern very well.

A minimal “write code -> run -> feedback” example​

conversation = [
    {"from": "planner", "to": "coder", "content": "Please write a discount calculation function"},
    {"from": "coder", "to": "executor", "content": "def discount(price): return price * 0.7"},
    {"from": "executor", "to": "reviewer", "content": "Execution result: discount(100)=70"},
    {"from": "reviewer", "to": "coder", "content": "Please add invalid input handling"}
]

for turn in conversation:
    print(turn)

Expected output:

{'from': 'planner', 'to': 'coder', 'content': 'Please write a discount calculation function'}
{'from': 'coder', 'to': 'executor', 'content': 'def discount(price): return price * 0.7'}
{'from': 'executor', 'to': 'reviewer', 'content': 'Execution result: discount(100)=70'}
{'from': 'reviewer', 'to': 'coder', 'content': 'Please add invalid input handling'}

This example already looks very similar to the workflow skeleton in many AutoGen tutorials.

What is the real advantage of AutoGen?​

It expresses “back-and-forth collaboration among multiple roles” very naturally​

It is especially good at expressing:

  • planner <-> worker
  • writer <-> reviewer
  • coder <-> executor <-> critic

These multi-round feedback relationships.

It is very friendly for prototyping and experimentation​

Because you do not necessarily need to fully draw out the state graph from the beginning. You can first:

  • Define several roles
  • Let them start talking
  • Then observe how the system progresses

This is very valuable during the exploration stage.

But you also need to understand the risks of the AutoGen style​

The number of message rounds can easily get out of control​

Once the system mainly relies on message back-and-forth to move forward, it is easy to end up with:

  • Too many rounds
  • Repeated discussions
  • Continuing to talk even though the task already has enough information

Role boundaries can drift easily​

If you do not define clear boundaries for each role, then you may see:

  • The planner starts writing code
  • The reviewer starts doing retrieval

As a result, role responsibilities become increasingly messy.

The stopping condition must be very clear​

Without a rule for “when to stop,” the system can easily keep running longer and longer.

So one very important engineering principle for AutoGen is:

Conversation can be natural, but the termination condition must be explicit.

What is the difference between it and CrewAI / LangGraph?​

Difference from CrewAI​

CrewAI emphasizes:

  • Roles
  • Tasks
  • Teams

AutoGen emphasizes:

  • How messages flow between roles

So a rough but memorable distinction is:

  • CrewAI is more like a “team schedule”
  • AutoGen is more like “team chat collaboration”

Difference from LangGraph​

LangGraph emphasizes:

  • Explicit state
  • Nodes
  • Conditional edges

AutoGen emphasizes:

  • Conversation turns
  • Round-by-round progress

So AutoGen feels more natural when expressing systems that “move the task forward like a conversation.”

When is it worth considering AutoGen?​

It is especially suitable for:

  • Multi-round negotiation tasks
  • Code generation + execution + feedback
  • Writing + review + revision
  • Prototyping and exploratory experiments

It may not be especially suitable for:

  • Production systems that require strict state-machine control
  • Processes with complex branching that must be tightly controlled

In other words, it is more like:

A framework that is very good at expressing “conversational collaboration.”

A very practical engineering reminder​

If you really want to build an AutoGen-style system in depth, it is best to add these early:

  • trace
  • maximum number of turns
  • role permission boundaries
  • failure fallback

Otherwise, the system can easily slide from:

  • Looking smart

to:

  • Very talkative, but inefficient

Summary​

The most important thing in this section is not to memorize the name AutoGen, but to understand:

It is best at expressing systems where multiple roles take turns advancing a task through conversation.

When a task naturally feels like group-chat collaboration, this kind of framework feels very natural. But if you need strong state control, you need to be especially careful about turn count and convergence.

Exercises​

  1. Design a 3-role message flow for planner -> coder -> reviewer.
  2. Think about why AutoGen-style tasks are especially prone to “too many rounds of talking.”
  3. Explain in your own words: what is the core difference between AutoGen and CrewAI?
  4. If your task requires strong state-machine control, would you still prioritize this conversational abstraction? Why?