Context Engineering: From Prompts to Corporate Multi-Agent Architecture
arXiv:2603.09619v1 Announce Type: new Abstract: As artificial intelligence (AI) systems evolve from stateless chatbots to autonomous multi-step agents, prompt engineering (PE), the discipline of crafting individual queries, proves necessary but insufficient. This paper introduces context engineering (CE) as a standalone discipline concerned with designing, structuring, and managing the entire informational environment in which an AI agent makes decisions. Drawing on vendor architectures (Google ADK, Anthropic, LangChain), current academic work (ACE framework, Google DeepMind's intelligent delegation), enterprise research (Deloitte, 2026; KPMG, 2026), and the author's experience building a multi-agent system, the paper proposes five context quality criteria: relevance, sufficiency, isolation, economy, and provenance, and frames context as the agent's operating system. Two higher-order disciplines follow. Intent engineering (IE) encodes organizational goals, values, and trade-off hierar
arXiv:2603.09619v1 Announce Type: new Abstract: As artificial intelligence (AI) systems evolve from stateless chatbots to autonomous multi-step agents, prompt engineering (PE), the discipline of crafting individual queries, proves necessary but insufficient. This paper introduces context engineering (CE) as a standalone discipline concerned with designing, structuring, and managing the entire informational environment in which an AI agent makes decisions. Drawing on vendor architectures (Google ADK, Anthropic, LangChain), current academic work (ACE framework, Google DeepMind's intelligent delegation), enterprise research (Deloitte, 2026; KPMG, 2026), and the author's experience building a multi-agent system, the paper proposes five context quality criteria: relevance, sufficiency, isolation, economy, and provenance, and frames context as the agent's operating system. Two higher-order disciplines follow. Intent engineering (IE) encodes organizational goals, values, and trade-off hierarchies into agent infrastructure. Specification engineering (SE) creates a machine-readable corpus of corporate policies and standards enabling autonomous operation of multi-agent systems at scale. Together these four disciplines form a cumulative pyramid maturity model of agent engineering, in which each level subsumes the previous one as a necessary foundation. Enterprise data reveals a gap: while 75% of enterprises plan agentic AI deployment within two years (Deloitte, 2026), deployment has surged and retreated as organizations confront scaling complexity (KPMG, 2026). The Klarna case illustrates a dual deficit, contextual and intentional. Whoever controls the agent's context controls its behavior; whoever controls its intent controls its strategy; whoever controls its specifications controls its scale.
Executive Summary
This article introduces context engineering (CE) as a standalone discipline in artificial intelligence (AI) development, building on the existing concept of prompt engineering. CE is concerned with designing, structuring, and managing the informational environment for AI agents to make decisions. The authors propose five context quality criteria and frame context as the agent's operating system. The article also introduces intent engineering and specification engineering as higher-order disciplines, which form a cumulative pyramid maturity model of agent engineering. The authors highlight the importance of controlling the agent's context, intent, and specifications for successful deployment and scale. With 75% of enterprises planning agentic AI deployment, the article reveals a gap between planning and execution due to scaling complexity. A case study illustrates a dual deficit in contextual and intentional control. The article emphasizes the need for a comprehensive approach to AI development, addressing the complexities of context, intent, and specifications.
Key Points
- ▸ Context engineering (CE) is a standalone discipline in AI development, concerned with designing, structuring, and managing the informational environment for AI agents.
- ▸ CE proposes five context quality criteria: relevance, sufficiency, isolation, economy, and provenance.
- ▸ Intent engineering and specification engineering are introduced as higher-order disciplines, forming a cumulative pyramid maturity model of agent engineering.
Merits
Strength
The article provides a comprehensive framework for AI development, addressing the complexities of context, intent, and specifications.
Interdisciplinary approach
The article draws on vendor architectures, academic work, enterprise research, and the author's experience, providing a rich and diverse perspective on AI development.
Demerits
Limitation
The article may be overly focused on large-scale enterprise deployment, neglecting smaller-scale or individual AI development projects.
Complexity
The article introduces several new concepts and disciplines, which may be challenging for non-experts to understand and implement.
Expert Commentary
The article provides a significant contribution to the field of AI development, highlighting the need for a comprehensive approach that addresses the complexities of context, intent, and specifications. However, the article may be overly focused on large-scale enterprise deployment, neglecting smaller-scale or individual AI development projects. Further research is needed to explore the practical implications of the article's framework and to develop concrete guidelines for AI developers and policymakers.
Recommendations
- ✓ Recommendation 1: AI developers and researchers should prioritize the development of comprehensive AI development frameworks that address the needs of context engineering, intent engineering, and specification engineering.
- ✓ Recommendation 2: Policymakers and governments should invest in AI education and training programs that prepare professionals for the challenges of context engineering, intent engineering, and specification engineering.