Introduction: More Than Just a Prompt

If you’ve spent any time working with modern AI, you’ve heard about the context window — the digital scratchpad where we feed information to large language models (LLMs) like GPT‑4 or Claude. We stuff it with prompts, documents, and chat history, hoping the model understands what we want. This practice, often called prompt engineering, or more broadly context engineering, feels like a skill born for the agent era.

But what if it isn’t new at all?

The research paper “Context Engineering 2.0: The Context of Context Engineering” argues that this challenge—making machines understand our situations and intentions—has been with us for decades. The techniques may have evolved, but the goal remains the same: to bridge the vast cognitive gap between human thought and machine logic.

Far from a passing fad, context engineering is portrayed as a long‑running discipline, advancing through distinct stages shaped by the rising intelligence of machines. The paper offers a rich historical narrative, a formal theoretical framework, and a glimpse of a future in which AI may understand our context better than we do ourselves.

Let’s unpack the true context of context engineering.

The Intelligence Gap: Why Context Engineering Exists

At its core, context engineering solves one foundational problem — the intelligence gap between humans (carbon‑based intelligence) and machines (silicon‑based intelligence). Humans communicate through shared experience, memory, and emotion, easily “filling in the gaps.” Machines cannot; ambiguity derails them.

The paper views context engineering as a process of entropy reduction. Human intention is inherently high‑entropy — messy, nuanced, and filled with implied meaning. Before machines can act on it, this information must be compressed and translated into low‑entropy representations. The work of reducing this entropy is the essence of context engineering.

Trajectories of human (carbon-based) and machine (silicon-based) cognitive abilities over time. The gap between them is the fundamental reason why context engineering is necessary.

Figure 2: The cognitive gap between human and machine intelligence drives the need for context engineering.

As machine intelligence accelerates, the nature of this gap evolves, reshaping how we design context. Technological breakthroughs lead to leaps in context assimilation, trigger interface revolutions, and ultimately redefine the paradigms of human‑machine collaboration.

The evolutionary cycle of context engineering, driven by technological breakthroughs that increase a machine’s ability to understand context, leading to new interfaces and new engineering paradigms.

Figure 3: Each rise in machine intelligence sparks a new interface revolution and paradigm shift.

The Four Eras of Context Engineering

This evolution follows a recurring pattern, summarized by the paper in four distinct eras. As shown in the overview below, we are currently in Era 2.0, transitioning toward Era 3.0.

An overview of the four eras of context engineering, from 1.0 to 4.0. As machine intelligence increases, its ability to process context grows, and the cost of human-AI interaction decreases.

Figure 1: More intelligence leads to higher context‑processing ability and lower interaction cost.

  1. Context 1.0 — Context as Translation: Humans manually translate their intentions into structured formats computers can parse — menus, command lines, and sensors.

  2. Context 2.0 — Context as Instruction: Intelligent agents interpret natural language and tolerate ambiguity. This is the era of LLMs and prompt engineering.

  3. Context 3.0 — Context as Scenario: AI reaches human‑level understanding, grasping nuanced social and emotional contexts and collaborating as true peers.

  4. Context 4.0 — Context as World: Superhuman AI begins not only to consume context but to construct it, revealing needs and insights we never articulated.

Formally Defining Context

To ground this discussion, the paper builds on early‑2000s research to provide a mathematical definition. The formulas may look technical, but the intuition is straightforward.

  • Entity \(e\): any participant relevant to an interaction—a user, application, environment, or object. The information describing that entity is its Characterization \( \mathrm{Char}(e) \).
\[ \mathrm{Char}: \mathcal{E} \to \mathcal{P}(\mathcal{F}) \]
  • Context \(C\): the union of all characterization information across relevant entities.
\[ C = \bigcup_{e \in \mathcal{E}_{rel}} \mathrm{Char}(e) \]
  • Context Engineering \(CE\): the optimization process mapping context and task to an effective processing function \(f_{context}\).
\[ CE:(C,\mathcal{T})\to f_{context} \]\[ f_{context}(C)=\mathcal{F}(\phi_1,\phi_2,\dots,\phi_n)(C) \]

Here, the operations \(\phi_i\) may include collecting, storing, transforming, selecting, sharing, or adapting context—regardless of era or technology. Whether a graphical interface from the 1990s or an agent in 2025, the challenge is consistent: ensuring that machines truly understand human intent.

The Historical Evolution of Context Engineering

Understanding the past helps illuminate the present. The comparison below highlights the most critical shifts between Context Engineering 1.0 and 2.0.

A table comparing the key characteristics of Context Engineering 1.0 and 2.0, highlighting the shift in technology, context modalities, and core mechanisms.

Table 1: Context 1.0 vs 2.0 — technological background and key mechanisms.

Era 1.0: Context as Translation (1990s – 2020)

Before chatbots and generative agents, researchers in Ubiquitous Computing and Context‑Aware Systems sought to make computers anticipate our needs. Because machines could not interpret natural language, designers acted as intention translators, converting human goals into structured signals such as location, time, or user activity.

Anind K. Dey’s landmark definition captured this era:

“Context is any information that can be used to characterize the situation of an entity…including the user and the applications themselves.”

Frameworks such as The Context Toolkit implemented this vision through modular components—widgets, interpreters, and services—each handling context acquisition, interpretation, and delivery. Though rule‑based and sensor‑driven, these systems laid the foundation for the more adaptive architectures that followed.

Era 2.0: Context as Instruction (2020 – Present)

The debut of GPT‑3 brought machine language understanding into everyday workflows. Designers no longer hard‑coded rules; they engineered contexts. Three transformations define the 2.0 epoch:

  1. Advanced Context Acquisition: Data now flows from smartphones, smartwatches, cameras, and even neural sensors.

A table showing the wide range of modern, multimodal context collectors, from smartphones and smartwatches to brain-computer interfaces.

Table 2: Representative multimodal context collectors.

  1. Tolerance for Raw Context: Modern systems ingest human‑native signals — text, audio, and imagery — without prior structuring, handling ambiguity naturally.

  2. From Awareness to Cooperation: Instead of static if‑then rules, systems analyze goals and assist directly in workflows. For example, an AI code assistant interprets your project and suggests an appropriate next function. Context shifts from reactive sensing to active collaboration — from context‑aware to context‑cooperative behavior.

Modern Design Principles for Context Engineering

The trajectory of context engineering today revolves around three pillars: collection and storage, management, and usage.

A mind map illustrating the key design considerations in modern context engineering, covering collection, storage, management, and usage with examples of various techniques and systems.

Figure 4: Major design considerations across collection, management, and usage.

1. Context Collection and Storage

Early systems gathered and stored context locally. Modern architectures distribute it across devices and the cloud, organized by temporal relevance:

  • Short‑term memory — fast, session‑level windows.
  • Long‑term memory — persistent data retained across sessions.

AI development tools such as Claude Code embody this approach: structured notes of progress are written to external memory, allowing the agent to resume work seamlessly even after interruptions.

2. Context Management

Processing Multimodal Context

Intelligent systems must unify diverse inputs—text, images, audio—into a shared representation.

A workflow diagram showing how multimodal inputs (image, text, audio) can be fused. Each modality is encoded into a vector, projected into a shared space, and then combined using techniques like cross-attention before being fed to a generation model.

Figure 5: Workflow for multimodal context fusion.

Common solutions include:

  • Shared Vector Spaces: Map each modality into a common embedding space for direct comparison.
  • Cross‑Attention: Allow one modality (e.g., text) to selectively attend to another (e.g., image regions) for context alignment.
Organizing Context: Layered Memory & Isolation

Andrej Karpathy likens an LLM to a CPU with its context window as RAM — rapid but limited. A hierarchical memory model addresses this constraint.

\[ M_s=f_{short}(c\in C:w_{temporal}(c)>\theta_s) \]

\[ M_l=f_{long}(c\in C:w_{importance}(c)>\theta_l\land w_{temporal}(c)\le\theta_s) \]

\[ f_{transfer}:M_s\to M_l \]

Short‑term memory captures immediate context; long‑term memory retains important abstractions; and context transfer consolidates the two. Systems also employ context isolation — delegating tasks to specialized sub‑agents with separate memory domains to prevent cross‑contamination.

Context Abstraction: The Art of “Self‑Baking”

Over time, raw logs and dialogue histories balloon. To stay efficient, agents abstract them into compact structures—a practice termed self‑baking.

A diagram showing four representative designs for context abstraction, or “self-baking”: using natural language summaries, direct structured storage, vector embeddings, and fixed-schema knowledge graphs.

Figure 6: Common designs for self‑baking and context abstraction.

Examples include:

  • Natural‑Language Summaries — periodic text condensations of recent activity.
  • Fixed Schemas — structured representations such as knowledge graphs or task trees; e.g., CodeRabbit builds a schema of project dependencies before reviews.
  • Vector Embeddings — compress long histories into semantic vectors, enabling efficient retrieval though less human‑readable.

3. Context Usage

Sharing Context Among Agents

Multi‑agent frameworks thrive on effective context exchange.

An infographic illustrating three common patterns for cross-agent context sharing: embedding context directly into prompts, exchanging structured messages with a predefined schema, and using a shared memory space (like a blackboard or graph) for indirect communication.

Figure 7: Typical modes of cross‑agent context sharing.

Three dominant patterns appear:

  • Embedding in Prompts: One agent’s output becomes the next agent’s input.
  • Structured Messaging: Agents exchange data via fixed schemas (JSON, APIs).
  • Shared Memory Spaces: Agents coordinate indirectly through a central “blackboard” or semantic graph.
Selecting the Right Context

Not all stored information should be surfaced. Effective context selection acts as “attention before attention,” filtering data by semantic relevance, logical dependency, and recency. Without this, agents risk context overload, hindering reasoning efficiency.

Proactive Inference

Finally, truly advanced agents move from reactive to proactive. They infer unstated user goals and act accordingly. If you frequently ask about Python optimization, an intelligent assistant might suggest best‑practices documentation before you even request it.

The Final Frontier: Lifelong Context and the Semantic OS

The next great challenge is lifelong context preservation — how to maintain a coherent, evolving record of our interactions over years.

Major obstacles include:

  • Storage Bottlenecks: Retaining vast, meaningful histories under finite resources.
  • Processing Degradation: Transformer attention weakens with sequence length.
  • System Instability: Accumulated errors amplify across long memories.
  • Evaluation Difficulty: Verifying reasoning over extended timelines.

Incremental improvements are insufficient; this demands a semantic operating system for context — a durable cognitive infrastructure capable of storing, retrieving, updating, and even forgetting information safely. Such systems must explain their reasoning chains to earn human trust, bringing machines one step closer to active cognition rather than passive storage.

Conclusion: From Tool to Collaborator

“Context Engineering 2.0” reframes what many assume to be a new craft as a long‑standing discipline. Rooted in decades of human‑computer interaction research, it has evolved through successive waves of machine intelligence while pursuing the same mission: to bridge intent and understanding.

We now inhabit the Instruction Era, guiding our agents with crafted prompts and workflows. Ahead lies the Scenario Era, where AI comprehends the full richness of human context, and beyond that the speculative World Era, where superhuman systems shape context itself, helping us discover new dimensions of our own thought.

As Marx wrote, “the human essence is the ensemble of social relations.” In the digital age, our essence may likewise be the ensemble of our contexts—a living, evolving reflection of our cognition and creativity. The story of context engineering is ultimately a story of what it means to be understood in an intelligent world.