The article explores context engineering as the critical practice of managing AI agent performance through precise information shaping. Effective agents don't simply process more data—they focus on the right information at the right time. Context engineering involves four key approaches: offloading information to external systems, retrieving data dynamically, isolating context for specific tasks, and reducing history when needed.

A major challenge is context rot, where performance degrades as context windows fill despite technical capacity. This stems from transformer architecture constraints creating n² interaction patterns. Context compaction offers a solution by summarizing contents when approaching limits, though difficulty lies in deciding what information should persist. Recent work on context folding provides alternative approaches for active working context management.

The article distinguishes between models and agents, introducing the concept of an agent harness—the deterministic shell around a stochastic core that manages context assembly. In multi-agent systems, communication through structured outputs rather than shared memory avoids KV cache penalties. Tool choice emerges as a context problem disguised as capability, with smaller, relevant toolsets improving navigation of complex action spaces.