Context Engineering 2.0: The Context of Context Engineering

2025-10-30

AI Summary

AI systems exhibit insufficient understanding of human context and intent. Method: This study systematically defines “context engineering” for the first time and traces its two-decade evolution, integrating philosophical inquiry, historical analysis, human–computer interaction (HCI) design principles, and social relational theory to develop a capability-driven, stage-based HCI theoretical model and propose a foundational theoretical framework for context engineering in AI systems. Contributions/Results: (1) It establishes context engineering as an independent research domain by delineating its conceptual boundaries and core theoretical tenets; (2) it identifies the dynamic evolution of context modeling as AI autonomy increases; and (3) it provides transferable conceptual tools and standardized methodologies for context design, enabling AI systems to transition from reactive behavior to contextual awareness. This work advances the theoretical foundations and practical design paradigms for context-aware intelligent systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Humans and AI: Understanding People, Theories, Concepts and Methods
• Philosophy and Ethics of AI: Philosophical Foundations of AI

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• How machines can better understand human situations and purposes
• Defining context engineering's historical evolution and conceptual landscape
• Establishing design principles for context engineering in AI systems

Innovation

• Defines context engineering for AI systems
• Traces evolution from human-computer interaction
• Outlines design considerations for practical implementation