Agentic coding struggles with variance due to the stochastic nature of large language models (LLMs). Each run can yield different results, even with the same context. A single agent might hit a peak performance or settle for a mediocre output, leaving potential insights unexplored. The author proposes using parallel agents to address this problem. By running multiple agents simultaneously, you can sample various paths and converge on better solutions. This approach not only increases the chances of finding optimal outcomes but also reduces reliance on luck. The article outlines two main workflows for using parallel agents. In the first phase, multiple agents generate different solutions to a problem, reducing the risk of relying on a single agent's output. For example, if debugging a UI issue, agents might analyze data flow, component layering, and React hooks from different angles. In the second phase, the focus shifts to gathering distinct information related to a problem. Agents can independently explore various sources like git history, documentation, and web research, ensuring a comprehensive understanding of the issue. The author shares a practical example from an AI hedge fund project, where they needed to improve model evaluations. By deploying four parallel agents, each examining different aspects of the problem, they all converged on adding calibration guidance to enhance output realism. This collaborative approach allowed for a more effective solution than any single agent could provide. The results highlight the value of parallel convergence in both generating solutions and gathering relevant information.