Context Engineering 2.0: The Context of Context Engineering (2510.26493v1)

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.

• The paper presents a comprehensive framework that conceptualizes context engineering as an entropy reduction process bridging human intent and machine representation.
• It formalizes context handling using set-theoretic methods and compares historical HCI trends with modern multimodal and proactive systems.
• The work outlines design choices for context collection, management, and usage while addressing scalability and autonomous AI integration challenges.

Context Engineering 2.0: The Context of Context Engineering

Introduction and Historical Perspective

The paper presents a comprehensive theoretical and practical framework for context engineering, situating it as a discipline that has evolved over decades in tandem with advances in machine intelligence. Rather than viewing context engineering as a recent innovation of the agent era, the authors trace its lineage to early human-computer interaction (HCI) and context-aware systems, emphasizing its foundational role in bridging the cognitive gap between human intent and machine understanding. The central thesis is that context engineering is fundamentally an entropy reduction process, transforming high-entropy, ambiguous human contexts into low-entropy, machine-interpretable representations.

Figure 1: The progression from context engineering 1.0 to 4.0, showing that increased intelligence yields greater context-processing ability and reduced human-AI interaction cost.

The historical analysis delineates four eras:

• Context Engineering 1.0: Primitive computation, structured inputs, rigid interfaces
• Context Engineering 2.0: Agent-centric intelligence, natural language understanding, ambiguity tolerance
• Context Engineering 3.0: Human-level intelligence, nuanced context assimilation
• Context Engineering 4.0: Superhuman intelligence, proactive context construction

  Figure 2: Divergent trajectories of carbon-based (human) and silicon-based (machine) cognitive abilities, motivating the need for context engineering.

Theoretical Framework and Formalization

The authors formalize context engineering using set-theoretic and functional notation, generalizing Dey’s definition of context. Entities ($\mathcal{E}$) and their characterizations ($\mathcal{F}$) are mapped via a situational characterization function, and context ($C$) is defined as the union of characterizations over relevant entities. Context engineering is then the process $$\text{CE}: (C, \mathcal{T}) \rightarrow f_{\text{context}}$$, where $f_{\text{context}}$ is a flexible composition of context processing operations ($\{\phi_i\}$), including collection, storage, representation, multimodal fusion, self-baking, selection, sharing, and dynamic adaptation.

This abstraction is intentionally technology-agnostic, applicable to both legacy HCI systems and modern LLM-based agents. The entropy reduction perspective is central: as machine intelligence increases, the cost and effort of context engineering decrease, and the system’s tolerance for raw, high-entropy context improves.

Figure 3: The evolutionary process in context engineering, highlighting paradigm shifts driven by advances in machine intelligence.

Design Considerations: Collection, Management, Usage

Context Collection and Storage

The paper details the expansion of context collection modalities, from simple sensors (location, time, device state) in 1.0 to rich multimodal signals (text, image, audio, physiological data) in 2.0. Storage architectures have evolved from local, ephemeral caches to layered, distributed systems supporting long-term, cross-device synchronization. The authors advocate for the Minimal Sufficiency Principle (collect only what is necessary) and the Semantic Continuity Principle (preserve meaning, not just data).

Context Management

Textual context processing strategies are compared:

• Timestamping: Preserves order, but lacks semantic structure
• Functional/Semantic Tagging: Improves retrieval, but can be rigid
• QA Pair Compression: Efficient for retrieval, but disrupts narrative flow
• Hierarchical Notes: Tree-structured, but may miss logical connections

Multimodal context fusion is addressed via three main strategies:

• Shared Vector Space Mapping: Project modalities into a common embedding space
• Joint Self-Attention: Unified processing of modality-specific tokens
• Cross-Attention: Direct inter-modality querying

  Figure 4: Example workflow for processing multimodal context with hybrid strategies.

Memory architectures are formalized as layered (short-term, long-term), with explicit transfer functions and selection criteria (temporal relevance, importance). Context isolation via subagents and lightweight references is highlighted as a means to mitigate context pollution and scale reasoning.

Context Abstraction (Self-Baking)

Self-baking is defined as the agent’s ability to abstract raw context into persistent, structured knowledge. Four patterns are described:

• Natural-Language Summaries: Flexible, but unstructured
• Schema-Based Fact Extraction: Structured, but extractor design is challenging
• Hierarchical Memory Vectors: Compact, but not human-readable
• Multilevel Summarization: Supports scalable reasoning

  Figure 5: Representative designs for self-baking in Era 2.0.

Context Usage

Intra-system and cross-system context sharing are analyzed, with patterns including prompt embedding, structured message exchange, and shared memory/blackboard architectures. Context selection is framed as a multi-factor process (semantic relevance, logical dependency, recency, frequency, redundancy, user feedback), with empirical evidence that excessive context degrades performance. Proactive inference of user needs is discussed, leveraging historical interaction data and chain-of-thought reasoning.

Figure 6: Common patterns of cross-agent context sharing.

Applications

The framework is instantiated in several domains:

• CLI Agents: File-system-based context organization (e.g., GEMINI.md), hierarchical inheritance, and compression via AI-generated summaries
• Deep Research Agents: Long-horizon reasoning, periodic context summarization, and evidence chain construction
• Brain-Computer Interfaces: Direct neural signal collection, expanding context dimensions beyond observable behavior

Challenges and Future Directions

The paper identifies several open challenges:

• Context Collection: Need for richer, more natural modalities (e.g., BCIs)
• Storage/Management: Scalability, semantic robustness, and latency
• Model Understanding: Limitations in semantic/logical reasoning, especially in multimodal contexts
• Long Context Processing: Quadratic complexity bottlenecks in transformers, need for new architectures (e.g., Mamba, LongMamba, LOCOST)
• Context Selection: Adaptive, precise filtering to avoid overload
• Lifelong Context: Semantic operating systems for dynamic, explainable, and human-like memory management

The authors speculate that as machine intelligence approaches and surpasses human cognition, context engineering will shift from explicit human-driven management to autonomous, proactive context construction by AI systems. The notion of “digital presence” is introduced, where persistent, evolving digital contexts become a form of identity and memory.

Conclusion

This work provides a rigorous, historically grounded, and forward-looking framework for context engineering, emphasizing its role as a discipline that adapts to the evolving capabilities of machine intelligence. By formalizing context engineering and analyzing its design considerations across collection, management, and usage, the paper offers actionable guidance for building scalable, adaptive, and semantically robust AI systems. The trajectory outlined suggests a future where context engineering becomes increasingly autonomous, with machines not only understanding but actively shaping human contexts.