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

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