Context Engineering 2.0: The Context of Context Engineering

TL;DR

This paper traces the evolution of context engineering from early human-computer interactions to future prospects involving superhuman AI, providing a systematic framework and design considerations for advancing AI understanding of human situations.

Contribution

It offers a comprehensive historical and conceptual analysis of context engineering, defining its scope and outlining key design principles for future AI systems.

Findings

Context engineering has evolved over three decades with increasing machine intelligence.

A systematic definition and conceptual landscape of context engineering are provided.

Future directions include achieving human-level or superhuman context understanding in AI.

Abstract

Karl Marx once wrote that ``the human essence is the ensemble of social relations'', suggesting that individuals are not isolated entities but are fundamentally shaped by their interactions with other entities, within which contexts play a constitutive and essential role. With the advent of computers and artificial intelligence, these contexts are no longer limited to purely human--human interactions: human--machine interactions are included as well. Then a central question emerges: How can machines better understand our situations and purposes? To address this challenge, researchers have recently introduced the concept of context engineering. Although it is often regarded as a recent innovation of the agent era, we argue that related practices can be traced back more than twenty years. Since the early 1990s, the field has evolved through distinct historical phases, each shaped by the intelligence level of machines: from early human--computer interaction frameworks built around primitive computers, to today's human--agent interaction paradigms driven by intelligent agents, and potentially to human--level or superhuman intelligence in the future. In this paper, we situate context engineering, provide a systematic definition, outline its historical and conceptual landscape, and examine key design considerations for practice. By addressing these questions, we aim to offer a conceptual foundation for context engineering and sketch its promising future. This paper is a stepping stone for a broader community effort toward systematic context engineering in AI systems.