Anthropic’s Claude models and Claude Code: usage, configuration, Skills, Hooks, pitfalls, and version changes.
Anthropic's applied AI team argues that context engineering is a continuation of prompt engineering, and the core idea is picking the smallest set of high-signal tokens within a limited attention budget.
Why it matters: Anthropic lays out a systematic approach to context engineering, covering the trade-offs among three long-task strategies: compression, note-taking, and sub-agents.
In his article, Lance Martin groups context engineering for agents into four strategies: writing (using scratchpads and memory to store information outside the context window) and selecting (pulling in memory, tool descriptions, and knowledge on demand).
Why it matters: The article groups agent context management into four strategies—writing, selecting, compressing, and isolating—and shows how various products put them into practice, making it easy to compare against your existing workflow.