Context Engineering is a critical discipline for AI agents, focusing on delivering the right information and tools to large language models (LLMs) in a way that maximizes task performance. The concept is expanded in this article through insights shared by Peak Ji from Manus and Lance Martin from LangChain. Key challenges include "Context Rot," which occurs when the LLM's performance deteriorates as the context window fills, and "Context Pollution," where irrelevant information clutters the input. Effective strategies to tackle these issues involve Context Compaction and Summarization, ensuring that only essential information is retained while still allowing the LLM to function optimally. The article emphasizes the importance of managing multi-agent systems. Sharing context among sub-agents leads to inefficiencies and confusion. Instead, the approach advocated is to create fresh sub-agents for discrete tasks with their own context, minimizing the shared memory dependency. A small toolset is also vital; providing an LLM with too many tools results in "Context Confusion." The hierarchical action space proposed by Manus organizes tools into levels, helping maintain clarity and efficiency in the model's operations. Agents should be treated as tools rather than anthropomorphized entities. This perspective simplifies the interaction between agents and the main model, allowing for structured calls that flatten complexity. The article outlines best practices, such as avoiding dynamic fetching of tool definitions, defining a pre-rot threshold to monitor token count, and using a dedicated planning sub-agent to reduce token waste. Security measures, including sandbox isolation and manual confirmation for sensitive actions, are also critical to managing risks effectively. The focus is on practical implementation and adapting to the rapidly evolving AI landscape.