Quit Tokenmaxxing: 4 Context Engineering Techniques for AI Agents

Context engineering is one of the most important tools we have for building efficient AI agents. It's about being intentional with what information makes it into an agent's context window, what stays there, what gets removed or transformed, and how the agent gets the information it needs when it needs it. In this video, we break context engineering down into four practical patterns: compress what's old, externalize what's large, select what's relevant, and isolate what doesn't belong in the same context. We'll walk through each technique with working Strands code, plus a one-liner that gives you optimized context management defaults out of the box. Read the Blog: https://go.aws/46jc336 Agent Toolkit for AWS: https://go.aws/46UHocw Github Repo: https://go.aws/4ikM9mM Benchmark Blog: https://go.aws/4xgH4jj Follow AWS Developers! 🆇 X: https://go.aws/4yCKnCy 💼 LinkedIn: https://go.aws/4ikDu3N 0:00 Quit Tokenmaxxing 1:06 One Line: -55% Cost, 60% to 98% Accuracy 2:12 4 Categories: Compress, Externalize, Select, Isolate 3:19 Compress: Summarization 6:30 Trade-Off: Summarization is Lossy 7:08 Externalize 11:17 Select: Progressive Disclosure (Skills) 14:23 Isolate: Sub-Agent 19:20 BONUS: Prompt Caching 20:08 Back to the One Line #AWS #ContextEngineering #StrandsAgents