Applied AI Research
Does Multi-Agent Coordination Beat a Single Agent? We Tested It.
Client: Cardinal Element, published in The Lab
+15.2%Debate vs. single model4.71 vs 4.09 out of 5.0 recommendation quality, blind-scored
+25%Reasoning depthlargest dimension gain, alongside constraint awareness (+21%) and internal consistency (+19%)
97.7%Parallel synthesisof debate quality at 0.4% of the cost, the efficiency finding that shapes what we build
The Challenge
Every multi-agent pitch faces the same fair question: where's the evidence that coordinating multiple AI agents produces better recommendations than one strong model with a longer prompt? Process metrics, like agents spawned and constraints extracted, demonstrate the system is doing something, not that it's doing something useful.
Our Solution
We ran a controlled evaluation of five execution architectures, from a single agent call to full multi-round debate with constraint negotiation, across five strategic business questions, scored by a blind evaluator on seven quality dimensions. The full methodology and results are published in The Lab.
Results
+15.2% Debate vs. single model - 4.71 vs 4.09 out of 5.0 recommendation quality, blind-scored
+25% Reasoning depth - largest dimension gain, alongside constraint awareness (+21%) and internal consistency (+19%)
97.7% Parallel synthesis - of debate quality at 0.4% of the cost, the efficiency finding that shapes what we build
Ready for similar results?
Let's discuss how we can transform your growth architecture.
Start Your Growth Audit