Context Engineering 2.0 represents a paradigm shift in aligning AI with human cognition by minimizing the entropy between intent and machine interpretation. Moving beyond early systems where users adapted to machines, modern approaches leverage LLMs to create cooperative agents that proactively manage context. Key techniques include context abstraction—such as summarization, fact extraction, and vector compression—and strategies like sub-agents and external data referencing to optimize performance. As context windows strain under growing data loads, filtering by relevance and recency becomes critical. Systems now aim for lifelong semantic memory, though challenges in storage, speed, stability, and evaluation persist. Emerging coordination methods and potential brain-computer interfaces point toward AI that not only assists but evolves with users. Ultimately, personal digital contexts may outlive individuals, transforming into enduring digital identities—reshaping how we perceive selfhood in an age of persistent AI memory.