Team of Rivals for AI: Reliable Multi-Agent Systems with CrewAI

In the rapidly evolving world of AI, building reliable systems isn't just about having the smartest model—it's about collaboration. A fascinating new paper titled "If You Want Coherence, Orchestrate a Team of Rivals: Multi-Agent Models of Organizational Intelligence" (arXiv:2601.14351), submitted on January 20, 2026, by Gopal Vijayaraghavan, draws inspiration from human organizations to create more robust AI agents.

By treating AI components like a "team of rivals"—specialized agents with opposing incentives—we can catch errors, reduce biases, and achieve coherence without perfect individual parts.

In this article, I'll break down the paper's key ideas and show how you can implement them practically using CrewAI, an open-source framework for multi-agent systems. I'll include example code to get you started, so you can experiment on your own.

The Paper: Building Reliable AI Through Organizational Intelligence

The core premise of the paper is simple yet profound: AI agents, like humans, are fallible. They hallucinate, miscommunicate, and carry biases. Instead of firing them (or scrapping the model), we should hire them into a structured "organization" where checks and balances minimize flaws.

Key Concepts

• Team of Rivals: Borrowed from historical contexts (like Lincoln's cabinet), this involves agents with strict roles and conflicting incentives. For example, a planner might be optimistic, while a critic is skeptical with veto power. This dynamic catches errors early.
• Specialized Roles: Agents are divided into planners (who outline strategies), executors (who handle data/tools), critics (who review for issues), and experts (domain-specific advisors).
• Separation of Reasoning and Execution: To avoid context pollution, agents don't directly call tools or ingest raw data. Instead, they write code that runs remotely, with only summaries returned. This keeps reasoning clean and efficient.
• Error Interception: Through layered critiques and retries, the system achieves over 90% internal error catching before user exposure. In production tests on financial analysis, it hit 92% success with modest compute overhead (~38% extra cost).
• Tradeoffs: Reliability comes at a small hit to speed, but it's scalable and incrementally expandable.

The authors demonstrate this in a real system: a coordinated setup with remote code execution, showing how imperfect LLMs (large language models) can form a coherent whole. It's a blueprint for production-grade AI, emphasizing orchestration over raw intelligence.

If you're building agents for research, automation, or analysis, this paper argues for ensemble methods inspired by corporate teams.

CrewAI: A Practical Framework for Multi-Agent Orchestration

Enter CrewAI, an open-source Python library designed exactly for this: creating "crews" of AI agents that collaborate on tasks. It supports roles, goals, tools, and workflows (sequential, hierarchical, or looped via Flows). CrewAI directly aligns with the paper's ideas—you can define rivals (e.g., a critic with opposing views), separate reasoning from execution (via tools), and implement retries for reliability.

Why CrewAI fits:

• Role-Based Agents: Assign backstories and goals to encourage "rival" behaviors.
• Task Delegation: Agents hand off work, with context sharing for coherence.
• Error Handling: Built-in verbosity and Flows enable critique loops.
• Extensibility: Integrate LLMs like GPT-4o, add custom tools, and scale to production.

The official examples repo (on GitHub) has tons of real-world setups, from trip planners to stock analysts. Let's dive into two examples I crafted, inspired by the paper.

Getting Started: Setup Your Local Environment

Before running the example code, you'll need to set up your local environment properly. This ensures everything works smoothly and avoids conflicts with other Python projects. Follow these steps:

1. Install Python: If you don't have Python installed, download and install Python 3.10 or later from the official website (python.org). Verify by running python --version in your terminal.
2. Create a Virtual Environment (Recommended): This isolates dependencies. Open your terminal and run:

# create local folder 
mkdir crewAI-team-of-rivals-example1 && cd crewAI-team-of-rivals-example1

# create local env and activate it
python -m venv crewai-env && source crewai-env/bin/activate

3.Install CrewAI and Required Packages: In your activated environment, install CrewAI and its tools:

pip install crewai crewai-tools

If using OpenAI models (as in the examples), also install the LangChain integration:

pip install langchain-openai

Set Up Your API Key: The examples use OpenAI's API. Sign up for an OpenAI account if needed, generate an API key from their dashboard, and set it as an environment variable.

export OPENAI_API_KEY="your-api-key-here"

How does it works

A sequential crew mimics a simple planner-executor flow, a researcher gathers insights, then a writer crafts content—echoing the paper's separation of perception (research) from execution (writing).

Run this code into the folder

import os
from crewai import Agent, Task, Crew
from langchain_openai import ChatOpenAI  # Or use other LLMs

os.environ["OPENAI_API_KEY"] = "your-api-key-here"

llm = ChatOpenAI(model="gpt-4o")  # Or gpt-4o-mini for cheaper/faster

# Agents
researcher = Agent(
    role="Senior Researcher",
    goal="Uncover cutting-edge insights on the topic",
    backstory="You're a meticulous expert driven by accuracy and depth.",
    llm=llm,
    verbose=True,
    allow_delegation=False
)

writer = Agent(
    role="Professional Writer",
    goal="Craft compelling and clear narratives",
    backstory="You're a skilled storyteller who simplifies complex ideas.",
    llm=llm,
    verbose=True,
    allow_delegation=False
)

# Tasks
research_task = Task(
    description="Research the latest trends in quantum computing for 2026.",
    expected_output="A detailed bullet-point report with sources and key insights.",
    agent=researcher
)

write_task = Task(
    description="Write an engaging 800-word blog post based on the research report.",
    expected_output="A polished blog post in markdown format, with introduction, body, and conclusion.",
    agent=writer,
    context=[research_task]  # Passes research output as input
)

# Crew
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    verbose=2  # Detailed logging
)

result = crew.kickoff()
print(result)

Run this, and you'll see the agents collaborate step-by-step. It's a great starting point for tasks needing depth without complexity.

To embody the "team of rivals," add a critic agent for error interception. This setup reviews the writer's output, flagging issues—aligning with the paper's 90%+ error catching.

# ... (same imports and LLM setup as above)

# Additional Critic Agent (with "opposing incentives" for rigor)
critic = Agent(
    role="Skeptical Critic",
    goal="Detect errors, biases, hallucinations, and inconsistencies",
    backstory="You're a rigorous debater who challenges assumptions to ensure flawless output.",
    llm=llm,
    verbose=True,
    allow_delegation=False
)

# Tasks (extend the basic example)
# ... (research_task and write_task as before)

review_task = Task(
    description="""
    Critically review the output for:
    - Factual errors or hallucinations
    - Logical inconsistencies
    - Biases or missing perspectives
    - Clarity and completeness
    If approved, output 'APPROVED: Final version ready.'
    If issues found, output 'REVISIONS NEEDED:' followed by detailed fixes.
    """,
    expected_output="Approval or specific revision instructions.",
    agent=critic,
    context=[write_task]  # Reviews the writer's output
)

# Crew with review
crew = Crew(
    agents=[researcher, writer, critic],
    tasks=[research_task, write_task, review_task],
    verbose=2
)

result = crew.kickoff()
print(result)

# For automatic iteration: Use CrewAI Flows with a loop condition based on critic output
# (e.g., loop back to writer if "REVISIONS NEEDED" – see official Flows docs/examples)

This adds a layer of reliability: The critic acts as a rival, vetoing subpar work. In practice, extend with tools (e.g., a remote executor) to fully separate reasoning from heavy lifting.

The "Team of Rivals" paper and CrewAI show that coherence emerges from orchestration. Whether you're automating workflows, analyzing data, or building assistants, multi-agent systems reduce failures and scale gracefully.

Check out the paper on arXiv and CrewAI's docs for more.

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