Context Engineering: why AI agents degrade as their context grows

Anthropic describes context engineering as managing everything a model sees during inference, beyond the user's direct prompt. The article argues that agents often fail on long tasks not because the model weakens but because accumulated context becomes bloated and unmanageable. Citing a Chroma study of 18 models, it reports that accuracy drops unevenly as input length grows, and that common needle-in-a-haystack benchmarks overstate real-world performance. The piece outlines three techniques: compaction, structured note-taking into external files, and multi-agent setups. It also cautions that overly aggressive compaction can drop critical details, so constraints should be stored in separate state files.