Researchers have developed a framework to optimize token usage and context window management in multi-agent AI workflows. The approach, based on internal production data, involves six patterns that can be applied to improve performance. These include techniques such as context stratification, semantic caching, and inter-agent communication compression. In experiments, the framework reduced cold-load latency by 93% and cut token usage by an estimated 60-70%. The study also found that using a mix of high-relevance and low-relevance items in prompts can improve model performance. --- Why it matters: This work matters to AI engineers because it provides practical methods for improving the efficiency and reliability of multi-agent workflows, which are crucial for many real-world applications. By reducing token usage and latency, these techniques can help make AI systems more cost-effective and scalable. Source: https://arxiv.org/abs/2608.17188