Clara Chong describes context engineering as the practice of strategically shaping the information provided to AI agents to optimize performance. She identifies four core techniques: offloading information to external systems, dynamically retrieving data rather than pre-loading everything, isolating context between subtasks, and strategically reducing history while preserving critical constraints. The article explains that “context rot” — performance degradation as context windows fill — can be addressed through context compaction, where agents summarize prior work before starting fresh contexts, and stresses that multi-agent systems should communicate through minimal structured outputs rather than complete transcripts.
