Context Engineering 2.0: The Context of Context Engineering

Karl Marx once wrote that “the human essence is the ensemble of social relations” (Marx, 1845), 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 “developed” the concept of context engineering. Although it is often regarded as a recent innovation of the agent era, in fact, we argue that related practices can be traced back to over 20 years ago. Since the early 1990s, it has evolved through distinct historical phases, each shaped by its intelligence level of machines: from early human-computer interaction (HCI) frameworks built around primitive computers, to today’s human-agent interaction (HAI) paradigms driven by intelligent agents, and potentially to human-level or even superhuman intelligence in the future.

Introduction. In recent years, the rapid rise of large language models (LLM) and intelligent agents has drawn increasing attention to how context influences model behavior. Studies have shown that the content placed within the context window can significantly affect model performance (Liu et al., 2021). At the same time, there is growing demand for systems capable of multi-step reasoning and operating over long time horizons (Yao et al., 2023). These trends make one question central: how can we enable machines to better understand and act upon human intent through effective context mechanisms, especially in long-horizon tasks? To address this challenge, researchers have recently focused on context engineering: the practice of designing, organizing, and managing contextual information so that machines can act in ways that align with human intentions (Mei et al., 2025).

Discussion / Conclusion. Digital Presence Karl Marx once wrote that “the human essence is the ensemble of social relations” (Marx, 1845). In the era of context-centric AI, this idea takes on a new computational meaning: individuals are increasingly defined not by their physical presence or conscious activity, but by the digital contexts they generate: their conversations, decisions, and traces of interaction. These contexts can persist, evolve, and even continue to interact with the world through AI systems long after the departure of a person. The human mind may not be uploaded, but the human context can — turning context itself into a lasting form of knowledge, memory, and identity. In this paper, we explore the context of context engineering, arguing that it is not a sudden invention of the LLM era but a long-evolving discipline shaped by the progressive intelligence of machines. By tracing its historical phases and by outlining design considerations that govern its practice, we highlight how the core challenge lies in bridging human intent and machine understanding under varying levels of entropy.