Effective Context Engineering for AI Agents
Source: https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents Author: Anthropic Engineering Date: 2026-02-24
Summary
Anthropic’s engineering blog post on context engineering — choosing the smallest, most relevant set of information for an LLM’s context window. Positions context engineering as the primary discipline for building effective agents, not prompt engineering.
Key Claims
- Context engineering > prompt engineering: the whole pipeline of what goes in (tools, memory, history, instructions) matters more than individual prompt phrasing.
- Key constraint: context windows aren’t infinite — what you exclude matters as much as what you include.
- Dynamic context construction: agents should add/remove context based on task state, not dump everything upfront.
- Four context sources: instructions, memory, tools, conversation history — each needs separate curation strategy.
- Token budget awareness: costs money and degrades attention quality past certain context lengths (see context rot).
- Anthropic’s recommendation: use structured formats (XML, JSON) for tool outputs; natural language for instructions.
Takeaways
Context engineering is infrastructure work, not prompt craft. The skill is knowing what to discard. This aligns with Letta’s Context Repositories approach (curated memory > full history).
Entities
Concepts
- Context Engineering — central topic, Anthropic’s framework
- Agent Memory — what persists between context windows
- Coding Agents — primary use case discussed