一个任务50次调用，成本狂砍90%？Manus首次公开上下文工程秘诀，一堆反复重写换来的教训

Core Insights - The article emphasizes the importance of context engineering in developing AI agents, highlighting the need for rapid iteration and improvement in response to evolving models and technologies [1][2]. Group 1: KV Cache Design - KV cache hit rate is identified as the most critical metric for AI agents in production, directly impacting latency and cost [4]. - The average input to output token ratio in Manus is approximately 100:1, which significantly benefits from KV caching, reducing the cost of cached input tokens to $0.30 per MTok compared to $3 per MTok for uncached tokens [5]. - Key practices to improve KV cache hit rate include maintaining stable prompt prefixes, appending content only, and marking cache breakpoints explicitly [8][9][10]. Group 2: Tool Management - As agents develop more capabilities, the complexity of the action space increases, leading to potential inefficiencies if tools are dynamically added or removed during iterations [11][14]. - Manus employs a context-aware state machine to manage tool availability without removing tools, thus preventing confusion and maintaining KV cache integrity [14][15][16]. Group 3: Context as a File System - The article discusses the limitations of context windows in modern large language models, suggesting that a file system can serve as an infinite context, allowing agents to read and write files as structured external memory [21]. - Manus implements a recoverable compression strategy, retaining essential information like URLs while allowing for context length reduction [24]. Group 4: Attention Manipulation - Manus uses a "todo.md" file to keep track of tasks, which helps maintain focus and avoid losing sight of goals during complex tasks [26][30]. - Retaining errors in the context is proposed as a method to improve agent behavior, allowing the model to learn from mistakes and reduce the likelihood of repeating them [32][35]. Group 5: Sample Diversity - The article warns against the pitfalls of few-shot prompting in agent systems, which can lead to repetitive and suboptimal actions [36]. - Introducing structured variations in actions and observations can help break patterns and adjust the model's attention, enhancing overall performance [37][38]. Group 6: Conclusion - Context engineering is deemed essential for AI agents, influencing their speed, recovery capabilities, and scalability [39]. - The future of agents will focus on constructing context effectively, underscoring the importance of thoughtful design [40].