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