Effective context engineering for AI agents
AI Summary
Anthropic's September 2025 framing post, published alongside Claude Sonnet 4.5 and the memory tool beta. The core move is to stop talking about prompts and start talking about the attention budget: what's the smallest set of high-signal tokens that gets the model to do the right thing? Memory enters as 'structured note-taking,' with concrete examples from Claude Code and Claude playing Pokémon. This is the piece that made 'context engineering' the default frame for 2026, absorbing agent memory into a bigger category.
Original excerpt
After a few years of prompt engineering being the focus of attention in applied AI, a new term has come to prominence: context engineering. Building with language models is becoming less about finding the right words and phrases for your prompts, and more about answering the broader question of “what configuration of context is most likely to generate our model’s desired behavior?"
Context refers to the set of tokens included when sampling from a large-language model (LLM). The engineering problem at hand is optimizing the utility of those tokens against the inherent constraints of LLMs in order to consistently achieve a desired outcome. Effectively wrangling LLMs often requires _thinking in…
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Content attributed to the original author (Prithvi Rajasekaran, Ethan Dixon, Carly Ryan, Jeremy Hadfield (Anthropic Applied AI)). Burn 451 curates publicly available writing as a reading index. For removal requests, contact @hawking520.