Large Language Model (LLM) agents have demonstrated remarkable potential in tackling problems that demand deep reasoning and multi-step thinking. When a single agent can successfully solve a complex task, it’s natural to wonder: why not use multiple agents to handle even more difficult challenges? This idea has fueled a growing trend in AI research—multiagent systems—where several LLMs interact, debate, and collaborate to arrive at a solution. The promise of multiagent systems is compelling. By introducing diverse perspectives, agents can challenge each other’s assumptions, uncover blind spots, and correct flawed logic. In theory, this dynamic exchange should lead to more accurate, robust, and well-rounded outcomes. The idea is that if one agent is biased or mistaken, another can spot the error and steer the group toward the truth. This mirrors human collaboration—two minds are better than one, right? But recent evidence suggests that more agents don’t always mean better results. In fact, studies have shown that adding multiple agents can reduce accuracy, increase inconsistency, and introduce new sources of error. Why? Because each agent brings not only different reasoning patterns but also different biases, hallucinations, and confidence levels. When these agents debate, they don’t always converge on the truth—they often reinforce each other’s mistakes or amplify noise. One key issue is the “echo chamber” effect. When agents are too similar in training or reasoning style, they can end up validating each other’s flawed logic, especially if they’re all prone to the same kinds of hallucinations. Worse, the debate process itself can become a trap—agents may generate more plausible-sounding but incorrect arguments just to appear persuasive. The more rounds of interaction, the more likely the group is to drift from reality. Another problem is coordination. Multiagent systems require mechanisms to manage disagreements, assign roles, and decide when to stop debating. Without clear protocols, the system can get stuck in endless loops or fail to converge at all. Even when a solution is reached, it may be based on a consensus of flawed reasoning rather than truth. Moreover, the cost—both computational and time—of running multiple agents often outweighs the benefits. For many tasks, a single, well-structured agent with strong prompting, self-consistency checks, and retrieval-augmented reasoning performs just as well, if not better, than a multiagent debate. So what’s the alternative? Instead of relying on multiple agents to argue and negotiate, focus on improving the quality of a single agent’s reasoning. Techniques like chain-of-thought prompting, self-consistency, and step-by-step reflection have proven highly effective. Use external tools—search, code execution, or knowledge bases—to ground the agent in facts. And critically, design the system to detect uncertainty and flag when it should stop and ask for human input. In short, the allure of multiagent debate is strong—but it’s often a trap. More agents don’t mean better reasoning. In many cases, they mean more noise, more error, and higher cost. The most effective AI systems aren’t those that argue the most, but those that think the clearest.