Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals,

Context Engineering Context engineering curates the smallest high signal token set for LLM tasks. The goal: maximize reasoning quality while minimizing token usage. When to Activate 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 Core Principles 1. Context quality quantity High signal tokens beat exhaustive content 2. Attention is finite U shaped curve favors beginning/end positions 3. Progressive disclosure Load information just in time 4. Isolation prevents degradation Partition work across sub agents 5. Measure before optimizing Know your baseline Quick Reference Topic When to Use Reference Fundamentals Understanding context anatomy, attention mechanics [context fundamentals.md](./references/context fundamentals.md) Degradation Debugging failures, lost in middle, poisoning [context degradation.md](./references/context degradation.md) Optimization Compaction, masking, caching, partitioning [context optimization.md](./references/context optimization.md) Compression Long sessions, summarization strategies [context compression.md](./references/context compression.md) Memory Cross session persistence, knowledge graphs [memory systems.md](./references/memory systems.md) Multi Agent Coordination patterns, context isolation [multi agent patterns.md](./references/multi agent patterns.md) Evaluation Testing agents, LLM as Judge, metrics [evaluation.md](./references/evaluation.md) Tool Design Tool consolidation, description engineering [tool design.md](./references/tool design.md) Pipelines Project development, batch processing [project development.md](./references/project development.md) Key Metrics 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 Four Bucket Strategy 1. Write : Save context externally (scratchpads, files) 2. Select : Pull only relevant context (retrieval, filtering) 3. Compress : Reduce tokens while preserving info (summarization) 4. Isolate : Split across sub agents (partitioning) Anti Patterns Exhaustive context over curated context Critical info in middle positions No compaction triggers before limits Single agent for parallelizable tasks Tools without clear descriptions Guidelines 1. Place critical info at beginning/end of context 2. Implement compaction at 70 80% utilization 3. Use sub agents for context isolation, not role play 4. Design tools with 4 question framework (what, when, inputs, returns) 5. Optimize for tokens per task, not tokens per request 6. Validate with probe based evaluation 7. Monitor KV cache hit rates in production 8. Start minimal, add complexity only when proven necessary Scripts [context analyzer.py](./scripts/context analyzer.py) Context health analysis, degradation detection [compression evaluator.py](./scripts/compression evaluator.py) Compression quality evaluation

context-engineering