Five patterns for managing what goes into the context window, and when.

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Context engineering has matured into a core AI engineering discipline. Five key patterns now define how production agents manage their context windows: (1) Progressive Disclosure via Agent Skills loads instructions in tiers based on relevance rather than upfront; (2) Context Compression uses sliding window plus LLM summarization to shrink accumulated history; (3) Context Routing classifies queries to direct them to the right knowledge source before anything enters the context window; (4) Retrieval Evolution moves from fixed RAG pipelines to agent-controlled loops with Agentic RAG, Graph RAG, and Self-RAG; (5) Tool and Capability Management addresses the hidden token cost of MCP tool schemas (90 tools can consume 50K+ tokens). Each pattern addresses a different failure mode, and production systems layer all five together. Practical starting points are given for each scenario.

State of Context Engineering in 2026

Pattern 1: Progressive Disclosure and Agent Skills

Pattern 5: Tool and Capability Management