Summary

The article discusses the emerging field of context engineering for AI agents, emphasizing the importance of managing context as a finite resource. It contrasts context engineering with prompt engineering, highlighting the need for strategies that optimize the utility of tokens during LLM inference. The article explores techniques such as compaction, structured note-taking, and sub-agent architectures to maintain coherence over long-horizon tasks. It concludes by stressing the significance of thoughtful context curation to enhance agent performance.

💭 Your Thoughts

With all these ideas to resolve the LLM's context window problem, I feel the current LLM is not really AI, just a function to call, a very powerful function...

Data Problems Addressed