Effective context engineering for AI agents

What this post added

This post introduces and defines 'context engineering' as the natural progression of prompt engineering for building more capable AI agents. It details the challenges of managing context in multi-turn LLM interactions, including context rot and attention scarcity due to transformer architecture constraints. The post provides practical guidance on optimizing system prompts, tools, and examples for minimal yet informative context. It also highlights 'just-in-time' context retrieval as a key strategy for agents, enabling dynamic data loading and progressive disclosure, exemplified by Claude Code's approach to complex data analysis.