A new perspective highlighted “context engineering” as the system-level discipline needed to build reliable AI systems, reframing prompt engineering as only one subset of the broader problem. The approach focuses on orchestrating data, tools, memory, and governance so large language model applications can behave consistently in production. For teams building regulated or high-stakes AI workflows, the message shifts from better prompts to architecture and lifecycle controls—especially around what the model can access and how it maintains state over time. The work argues that reliability must be designed end-to-end, not debugged after failures occur.