context-engineering

Context engineering curates the smallest high-signal token set for LLM tasks. The goal: maximize reasoning quality while minimizing token usage. - Designing/debugging agent systems - Context limits constrain performance - Optimizing cost/latency - Building multi-agent coordination - Implementing memory systems - Evaluating agent performance - Developing LLM-powered pipelines - Context quality > quantity - High-signal tokens beat exhaustive content - Attention is finite - U-shaped curve favors beginning/end positions - Progressive disclosure - Load information just-in-time - Isolation prevents degradation - Partition work across sub-agents - Measure before optimizing - Know your baseline - Token utilization : Warning at 70%, trigger optimization at 80% - Token variance : Explains 80% of agent performance variance - Multi-agent cost : ~15x single agent baseline - Compaction target : 50-70% reduction, <5% quality loss - Cache hit target : 70%+ for stable workloads - Write : Save context externally (scratchpads, files) - Select : Pull only relevant context (retrieval, filtering) - Compress : Reduce tokens while preserving info (summarization) - Isolate : Split across sub-agents (partitioning) - Exhaustive context over curated context - Critical info in middle positions - No compaction triggers before limits - Single agent for parallelizable tasks - Tools without clear descriptions...