Context Engineering

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

• 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

Quick Reference

| Topic | When to Use | Reference |

|-------|-------------|-----------|

| Fundamentals | Understanding context anatomy, attention mechanics | context-fundamentals.md |

| Degradation | Debugging failures, lost-in-middle, poisoning | context-degradation.md |

| Optimization | Compaction, masking, caching, partitioning | context-optimization.md |

| Compression | Long sessions, summarization strategies | context-compression.md |

| Memory | Cross-session persistence, knowledge graphs | memory-systems.md |

| Multi-Agent | Coordination patterns, context isolation | multi-agent-patterns.md |

| Evaluation | Testing agents, LLM-as-Judge, metrics | evaluation.md |

| Tool Design | Tool consolidation, description engineering | tool-design.md |

| Pipelines | Project development, batch processing | 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

• 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)

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

• Place critical info at beginning/end of context
• Implement compaction at 70-80% utilization
• Use sub-agents for context isolation, not role-play
• Design tools with 4-question framework (what, when, inputs, returns)
• Optimize for tokens-per-task, not tokens-per-request
• Validate with probe-based evaluation
• Monitor KV-cache hit rates in production
• Start minimal, add complexity only when proven necessary

Scripts

• context_analyzer.py - Context health analysis, degradation detection
• compression_evaluator.py - Compression quality evaluation