Long-horizon search agents face significant challenges in managing their working context, which can grow exponentially as they process information and interact with their environment. To address this issue, researchers have proposed an adaptive context management approach, known as LongSeeker, which dynamically adjusts the level of detail retained for different parts of the agent's trajectory. This elastic context orchestration enables the agent to maintain a balance between retaining relevant information and avoiding overwhelming amounts of data. By implementing LongSeeker, search agents can reduce the risk of errors and decrease costs associated with storing and processing large amounts of data1. This development has significant implications for the field of artificial intelligence, particularly in applications where search agents are used to analyze complex systems or make decisions based on vast amounts of data. Effective context management is crucial for the reliability and efficiency of these agents, so practitioners must prioritize the development of adaptive solutions like LongSeeker to ensure the success of their search agent deployments.