Research into multi-agent AI systems shows that coordination can boost performance, with multi-agent systems demonstrating up to 81% performance improvements on certain parallelizable tasks, while poorly orchestrated independent setups can see error amplification up to 17.2 times higher. Using multiple model calls on stochastic large language models can improve accuracy on benchmarks like HumanEval, but complex agent architectures risk escalating computational expenses without proportional accuracy gains. A genuine near-doubling of accuracy appears in research published by Capital One, where AI models trained on tokenized patient data achieved nearly double the accuracy compared to those using traditional data masking, though this concerns data preparation, not answer sharing. Closing the accuracy gap is difficult because naive sharing mechanisms can dilute accuracy if agents lack a way to identify correct answers before amplifying them. Sophisticated approaches like voting, confidence weighting, or iterative refinement loops multiply inference costs and introduce latency, and performance on academic benchmarks does not always translate to real-world reliability.