𝗠𝗗𝗔𝗦𝗛: 𝗛𝗼𝘄 𝟭𝟬𝟬 𝗔𝗴𝗲𝗻𝘁𝘀 𝗕𝗲𝗮𝘁 𝗢𝗻𝗲 𝗙𝗿𝗼𝗻𝘁𝗶𝗲𝗿 𝗠𝗼𝗱𝗲𝗹

𝗠𝗗𝗔𝗦𝗛: 𝗛𝗼𝘄 𝟭𝟬𝟬 𝗔𝗴𝗲𝗻𝘁𝘀 𝗕𝗲𝗮𝘁 𝗢𝗻𝗲 𝗙𝗿𝗼𝗻𝘁𝗶𝗲𝗿 𝗠𝗼𝗱𝗲𝗹

Composition beats scale.

Microsoft recently released results for a system called MDASH. It scored 88.45% on the CyberGym security benchmark. This beat Anthropic's Mythos and OpenAI's GPT-5.5.

The secret is not a better model. The secret is using many models.

MDASH uses over 100 specialized agents. It builds a pipeline of different models to find code flaws. Some models reason. Others filter data. Some act as debaters.

This works because MDASH follows a five-stage process:

• Mapping: The system analyzes the code to find high-value areas.
• Auditing: Specialized agents find potential flaws and create theories.
• Debating: A second group of agents argues against those findings. This removes errors.
• Deduplication: The system groups similar findings to save time.
• Proving: Final agents build actual exploits to prove the flaw is real.

A single model often fails at complex tasks. It might see a bug in one function but miss how it connects to another file. A pipeline of specialists solves this. Each agent has one job.

The lesson for you is simple. Do not try to find one model that does everything.

If you want to build better AI systems, follow these rules:

• Separate discovery from validation. Do not ask one agent to find a bug and prove it at the same time.
• Use disagreement as a signal. When agents argue, you find the truth.
• Stay model-agnostic. Your system should work with any model. You should only need to change a config file to upgrade.
• Prove the result. Move from saying "this looks wrong" to "here is the proof."

The value is not in the model you rent. The value is in the system you build around it.

Source: https://dev.to/max_quimby/mdash-how-100-agents-beat-one-frontier-model-4e56

Optional learning community: https://t.me/GyaanSetuAi