Context Engineering: 3 Levels of Difficulty and Best Practices for AI Agents

Most of this is fairly intuitive after the first few mistakes, but why make them if you can read up beforehand? Several objects for different data types, ContextBuilder, and compression with extraction are found in any more or less complex “long-lived” agent. Even in https://github.com/korchasa/severin/tree/main/src/agent/context.

https://www.kdnuggets.com/context-engineering-explained-in-3-levels-of-difficulty

TL;DR

The LLM context window is not a bottomless pit; it’s a managed resource. For reliable AI agents, simply “stuffing everything into the context” isn’t enough. You need a systemic approach to what enters the window, how it’s compressed, and when it’s evicted.

3 Levels of Context Engineering Difficulty

Level 1: Understanding the Bottleneck

Every model has a limit. In agentic scenarios (multiple steps, API responses, documents), the context quickly fills with “noise.” The model begins to lose instructions, hallucinate, or lose its logical flow. Context management is the only way to make complex systems stable.

Level 2: Optimization Practices

• Token budgeting: Dividing the window into components (instructions, tool schemas, history, retrieval). This allows for conscious trade-offs, like sacrificing history for data accuracy.
• Compression: Instead of naive summarization, semantic or extractive compression is used — preserving key facts, commitments, and user intents.
• On-demand retrieval: Using the Model Context Protocol (MCP) to connect data sources only when the agent specifically requests information.

Level 3: Production Architecture

• Multi-layered Memory:
  • Working: the current window.
  • Episodic: compressed state of past steps.
  • Semantic: knowledge base (facts).
  • Procedural: dynamic instructions.
• Smart Retrieval: Hybrid search (Dense + BM25) with meta-filters. The Contextual Retrieval technique from Anthropic (adding context to chunks before embedding) radically reduces retrieval misses.
• Token-level profiling: Simplifying schemas (JSON instead of OpenAPI), deduplication, and hierarchical synthesis (extracting summaries from documents first, then the final response).