Multi-agent AI
Multi-agent systems assign different roles or models to a task. In a decision product, that only helps if the roles produce claims you can score and challenge.
feature
Several models, still one inspectable score
Argumont can involve more than one model. That helps when roles disagree. It does not mean five fluent answers have become a fact.
What it is for
Reducing the chance that a single prompt and a single model rubber-stamp your framing. Disagreement is useful data.
What it is not
A vote. Consensus tools optimize for agreement. Argumont optimizes for a score you can inspect, even when models line up.
Related pages
Versus consensus tools
Voting, dots, and alignment workshops are designed to produce agreement. Argumont is designed so a well-supported option can win even if it is initially unpopul...
Versus multi-agent chat
Multi-agent chat products put several personas in a thread. That can be engaging. Argumont is not trying to win as entertainment. The user should not need to un...
How it works
The product is a sequence, not a conversation that hopes to end well. Each step leaves something you can inspect.
Features
Each feature page describes something the product actually does, including limits. Team sharing beyond a private workspace is called out as future work.
See how analysis runs
Argumont challenges assumptions, researches evidence, compares competing approaches, and identifies the strongest decision.
See how analysis runs