How a 'Catfish Agent' is Disrupting Silent Agreement in AI-Powered Clinical Decision Making

The Problem with Silent Agreement in AI Medical Teams

Imagine a group of AI doctors reviewing a complex medical case. At first, they each propose different diagnoses—but then, something strange happens. Instead of debating the merits of each option, they fall into a hush. No one challenges the emerging consensus. No one asks tough questions. They silently agree on a diagnosis… and it’s wrong.

This phenomenon, dubbed "Silent Agreement," is a critical flaw in multi-agent AI systems designed for clinical decision-making. Researchers from The Chinese University of Hong Kong, Amazon, and the Shanghai Artificial Intelligence Laboratory have identified it as a major bottleneck in AI-assisted medicine—one that can lead to misdiagnoses when AI agents prematurely converge on answers without sufficient critical analysis.

Enter the Catfish Agent

Inspired by organizational psychology and the "catfish effect"—where introducing a disruptive element (like a catfish in a tank of sardines) keeps a group active and engaged—the team developed a novel solution: the Catfish Agent. This specialized AI role is designed to inject structured dissent into discussions among medical AI agents, forcing them to reconsider assumptions and defend their reasoning.

"Without contraries is no progression," the researchers quote William Blake in their paper. And in AI medicine, that means deliberately introducing tension to prevent groupthink.

How It Works

The Catfish Agent operates with two key mechanisms:

1. Complexity-Aware Intervention – The agent adjusts its level of engagement based on how difficult the case is. For simple cases, it might offer a light critique. For highly complex ones, it becomes a free-roaming challenger, adopting different medical personas (e.g., a skeptical oncologist or a meticulous radiologist) to probe weaknesses in the team’s reasoning.
2. Tone-Calibrated Intervention – Not all dissent is created equal. The Catfish Agent modulates its tone based on how strongly the group is converging:

• Mild interventions for early consensus (e.g., "Have we considered alternative explanations?")
• Strong interventions for uncritical agreement (e.g., "This reasoning ignores key lab results—let’s revisit the evidence.")

Results: Outperforming GPT-4o and Other Leading Models

The team tested their approach on 12 medical benchmarks, including MedQA, PubMedQA, and visual question-answering tasks. The results were striking:

• 39.2% relative improvement over the best prior model (DeepSeek-R1) on medical Q&A tasks.
• 12.7% gain over top multi-agent frameworks in medical visual question answering.
• Dramatic reduction in Silent Agreement failures—from 61-90% in existing systems to just 11-17% with the Catfish Agent.

Why This Matters for AI in Healthcare

AI-assisted diagnosis is already being piloted in hospitals worldwide. But if AI teams fall into the same traps as human ones—premature consensus, lack of debate, overlooked alternatives—then errors will persist. The Catfish Agent offers a way to keep AI medical teams sharp, ensuring that diagnoses are rigorously debated before final decisions are made.

The Future: More Dynamic AI Teams

The researchers suggest that future AI medical systems could benefit from even more dynamic dissent mechanisms, such as:

• Rotating Catfish roles to prevent predictability.
• Adaptive debate structures that adjust based on real-time confidence metrics.

For now, though, the Catfish Agent stands as a compelling proof-of-concept: sometimes, the best way to improve AI reasoning is to force it to argue with itself.