Quit Tokenmaxxing: 4 Context Engineering Techniques for AI Agents

Why watch

This video teaches four context engineering patterns for building efficient AI agents: compressing old information through summarization, externalizing large data to vector stores, selecting relevant context via progressive disclosure, and isolating unrelated tasks into sub-agents. The presenter demonstrates each pattern with working Strands code and shows how these techniques can reduce costs by 55% while improving accuracy from 60% to 98%. Developers building AI agents on AWS will learn practical, immediately applicable patterns for optimizing context management.