I Made AI Agents Debate About Nonsense – Here's What I Learned
How giving LLMs bizarre personas via Faker and CrewAI revealed surprising truths about agent design.
Ever feel like learning core AI concepts like "agents" involves wading through dense documentation and abstract diagrams? Yeah, me too. My brain sometimes craves something more... hands-on.
So, I tried something a little different. I decided to make AI agents argue. Not about anything sensible, mind you. My first prompts involved weaponized corporate jargon that resulted in pure, glorious nonsense.
What started as a playful coding session ("Can I make them do this?") unexpectedly morphed into the clearest lesson I've had on how AI agents work, especially using frameworks like CrewAI. Turns out, building slightly ridiculous Python bots is a fantastic way to learn!
Stick around, and I'll show you:
How I used Python, Faker, and CrewAI to build these digital debaters.
The surprisingly coherent arguments they produced (once I gave them a real topic!).
The key "aha!" moments about AI agent design.
How you can grab the code and build your own!
Step 1: Giving AI Agents Personality (With Python & Faker)
Just having an LLM generate text wasn't enough. I wanted distinct characters. Enter Faker, the brilliant Python library for generating fake data. It became my instant persona generator:
# Using Faker to quickly generate identity details
import random
from faker import Faker
from crewai import Agent # Make sure Agent is imported if used below directly
fake = Faker()
def generate_debater_identity() -> dict:
"""Generates a dictionary for a fictional debater."""
# Generate the name once to use it consistently
generated_name = fake.name()
return {
"name": generated_name, # Use the stored name
"job": fake.job(),
"personality": fake.sentence(nb_words=random.randint(8, 15)), # Random personality trait
"goal": fake.catch_phrase(), # Random goal/philosophy
# Use the *same* stored name in the backstory
"backstory": f"Known for their {fake.bs()} approach, {generated_name} believes deeply in {fake.company_suffix()} principles. {fake.paragraph(nb_sentences=2)}"
}
# In the main script, I call this function twice:
identity_1 = generate_debater_identity()
identity_2 = generate_debater_identity()This function instantly gives me everything needed: a name, job, personality quirk, a catchphrase goal, and a short, nonsensical backstory.
Step 2: Structuring the Chaos with CrewAI
With personas generated, I needed to turn them into functioning agents. CrewAI is a Python framework perfect for this. It lets you define agents with specific roles, goals, and backstories, guiding how the LLM behaves. Here’s how I plugged the Faker identity dictionary (identity) directly into a CrewAI agent definition:
# Simplified example of defining a CrewAI agent in the script
from crewai import Agent
# identity = result from generate_debater_identity()
# llm_instance = my configured LLM (like OpenAI GPT-4 or Ollama Llama 3)
debate_agent = Agent(
role=identity['job'], # Tells the agent its job/function
goal=f"Win the debate on '{topic}'. Argue persuasively based on your persona. Your core belief is: {identity['goal']}", # The agent's objective
backstory=f"You are {identity['name']}, a {identity['job']}. {identity['personality']}. {identity['backstory']}", # Context about who it is
llm=llm_instance, # Specifies which LLM 'brain' to use
verbose=False # Keeps the console output cleaner during the run
)This code tells CrewAI: "Create an agent. Use the generated Faker details for its role, goal, and backstory. Use this specific LLM to think and speak for it."
Meet the Contenders
For the specific debate example below, imagine the generate_debater_identity function produced something like these two glorious characters:
Name: Marsha Keller
Job: Professional Umbrella Opener
Personality: Speaks only in palindromes when under pressure.
Goal: "Brand dynamic thought showers."
Backstory: Known for their "aggregate scalable convergence" approach, Donnie Smith believes deeply in "LLC" principles. They claim to have won an argument against a goose using only shadow puppets. No one can prove it didn't happen.
Name: Denise Sanders
Job: Aquarium Cleaner
Personality: Always drinks soup during heated arguments without blinking.
Goal: "Synchronize strategic niches for tactical advantage."
Backstory: Known for their "recontextualizing vertical deliverables" approach, Johnnie Simmons believes deeply in "Group" principles. Their memoir was banned in three countries for including unauthorized jellyfish philosophy. After several failed balloon ventures, they turned to interpretive debate.
Step 3 : Topic selection and debate structure
With the agents created, the script needed two more things:
A Debate Topic: The script prompts the user to enter a topic. If the user just hits Enter, it uses Faker again to generate a random, jargon-filled topic like "Should [Company Name] be allowed to [Corporate BS Verbiage]?"
Running the Debate: The script orchestrates the debate turn by turn. It calls a function (run_debate_turn in my full code) that passes the topic, the agent's identity, the opponent's last statement, and the current turn description (e.g., "Opening Statement," "Rebuttal 1") to the CrewAI agent. The agent generates its response, which is then recorded and fed as input to the opponent for their next turn. This continues for a set number of rounds.
# --- Define Debate Topic ---
user_topic = input("Enter a debate topic (or press Enter for a random one): ").strip()
# Use a slightly more engaging random topic generator if none provided
topic = user_topic if user_topic else f"Should {fake.company()} be allowed to {fake.bs()}?"
print(f"\n💬 Debate Topic: {topic}\n")
# Example : Opening statements
statement_1 = run_debate_turn(agent_1, identity_1, None, topic, "Opening Statement")
debate_history.append(statement_1)
last_statement = statement_1
statement_2 = run_debate_turn(agent_2, identity_2, last_statement, topic, "Opening Statement / Rebuttal")
debate_history.append(statement_2)
last_statement = statement_2See my Github for the full code.
Step 4: Enjoy The Show
Debate Topic: Should Telospace Inc. be allowed to empower backward-compatible solutions?
Opening Statement: Marsha Keller (Professional Umbrella Opener)
"Able was I ere I saw Elba."
(She stands atop a rotating platform with an umbrella opening and closing in sync with her heartbeat.)
“Ladies and gentlemen, and sentient organisms unbothered by precipitation...
Today we gather beneath the thunderous possibility of innovation. Thought showers—yes, brand them, direct them, orchestrate the downpour.
As a Professional Umbrella Opener, I do not shy from storms—I choreograph them. And let it be known: static ideas shall never open new skies.”
Opening Statement: Denise Sanders (Aquarium Cleaner)
(She calmly opens a thermos and ladles miso soup into a bowl shaped like a jellyfish.)
“I do not blink. I do not stir.
When you clean aquariums, you learn—life thrives in silence and water. But the real art lies in niche synchronization.
Tactical advantage isn’t won with thunder; it's earned in ripples, in patterns only seen when the surface is still.
My opponent twirls umbrellas. I reveal currents.”
Rebuttal Round 1 – Marsha Keller
"Madam, in Eden, I'm Adam."
“My opponent speaks of ripples, but forgets the flood.
Tactical niche? You miss the torrential truth: synergy cannot be contained in tanks.
My umbrellas are not tools of defense—they are instruments of emergence.
Kayak salad, Alaska yak.”
Rebuttal Round 1 – Denise Sanders
(She refills her bowl without once averting her gaze.)
“Floods distort. I clarify.
My jellyfish philosophy remains: those who blink miss the bloom.
The aquarium does not fear the storm—because it already holds it.
Watch closely, dear Marsha, for even your umbrella casts a reflection.”
Final Thoughts:
What Making AIs Argue Really Taught Me
This wasn't just a fun coding exercise. Although the outputs are questionable, they are interesting and the whole thing demystified AI agents for me:
Persona is Everything: How you define an agent's identity and purpose (role, goal, backstory in CrewAI terms) fundamentally dictates its behavior. Garbage in, garbage out... but structured persona in? Coherent output out!
Frameworks Add the Scaffolding: Tools like CrewAI provide the essential structure that allows LLMs to act like specialized agents, coordinating their tasks and maintaining context.
LLMs Are Powerful Roleplayers: Give an LLM a clear persona and objective within a framework, and it can be remarkably good at adopting that role.
Learning Through Play Works: Honestly, debugging the weird outputs and seeing the agents almost make sense (even with joke prompts) taught me more intuitively about agent mechanics than any documentation page could.
Experiment with your own AI Debate Agents (Code Included)
Ready to orchestrate your own AI arguments? You absolutely should! It's a fantastic way to get a feel for AI agents, LLMs, and frameworks like CrewAI.
What happens if you change the Faker generators? (Try fake.catch_phrase() vs fake.bs()).
How do different LLMs handle the same personas?
Can you give them even more ridiculous debate topics?
Can you make your own personas and agent?
Can you make the agents do other things?
Embrace the Weirdness
Learning AI doesn't always have to be serious business. Sometimes, the most effective way to grasp complex concepts like AI agents is to roll up your sleeves, write some Python, and make things happen – even if that "thing" is a slightly absurd AI debate club.
So go ahead, experiment, build something fun, and see what you discover. You might surprise yourself.