Harness Engineering and SimpleModeling
Harness Engineering is an essential foundation in the AI era.While AI accelerates code generation, it also introduces structural instability and missing quality attributes. To control this, constraints and guarantees must be provided at the execution layer.
SimpleModeling connects BoK (Body of Knowledge), DSL (Domain Specific Language), and the execution platform into a single flow, providing a Harness that governs execution based on meaning. As a result, developers can focus on domain logic while maintaining consistent quality and reproducibility—even with AI-generated code.
Essence of Harness Engineering
Harness Engineering is an approach in which applications are executed through a controlled execution layer (the Harness), ensuring quality, reproducibility, and safety.
Its essence lies in executing software through a constrained environment rather than running it directly. On top of platforms provided by DevOps and Platform Engineering, it defines how execution is performed and ensures consistent quality.
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Centralize quality attributes in the framework
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Ensure reproducibility and safety
In environments where AI-generated code is prevalent, structure and quality attributes tend to be implicit, so constraints and guarantees at the execution layer are essential. Harness Engineering provides the foundation to control this instability and run generated code safely and consistently.
Extension by BoK
Traditional Harness Engineering primarily targets execution and operations. SimpleModeling extends this scope by introducing BoK.
Management of Meaning
BoK organizes concepts, relationships, and contexts into a structured knowledge base, allowing systems to be treated as meaning-rich structures rather than mere collections of code.
Integration with AI
BoK serves as a reasoning foundation for AI, enabling more accurate specification and code generation.
With this extension, the Harness evolves beyond execution control into a foundation that handles meaning.
Concept of Executable Harness
What SimpleModeling introduces is not just an extension of Harness Engineering, but a Harness as a predefined execution framework where domain structure and quality attributes are fixed.
In traditional development, domain structure, execution mechanisms, and quality attributes are not clearly separated, and in practice must be designed and implemented as a single whole. Experienced developers can consciously separate these concerns, but otherwise they tend to become entangled, resulting in complex and unstable implementations. AI-generated code shows a similar tendency toward such entangled structures.
SimpleModeling addresses this by clearly separating and fixing concerns: domain structure is defined by DSLs and model compilers, and quality attributes are handled by the execution platform (CNCF).
As a result, developers no longer need to manage structure and quality attributes themselves, and can focus on implementing domain logic.
This "execution framework with fixed structure and quality" is the essence of the Executable Harness. It can be seen as realizing Harness Engineering at the structural level.
Conclusion
While highly compatible with Harness Engineering, SimpleModeling extends its scope to include meaning itself. It is not just an execution platform, but a development foundation that treats meaning, specification, and execution as a unified whole. This approach allows developers to focus on domain logic while ensuring stable quality and reproducibility across the entire system, including AI-generated code. It represents a new foundational model for software development in the AI era.
References
Glossary
- BoK (Body of Knowledge)
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At SimpleModeling, the core knowledge system for contextual sharing is called the BoK (Body of Knowledge). The goal of building a BoK is to enable knowledge sharing, education, AI support, automation, and decision-making assistance.
- DSL (Domain Specific Language)
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A DSL (Domain-Specific Language) is a language designed for a particular domain, enabling direct and concise expression of the domain’s concepts and structures. Compared to general-purpose programming languages (GPLs), DSLs offer a higher level of abstraction tailored for domain-specific problem solving and automation.
- literate model
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A Literate Model is a “readable model” that integrates model structure with natural-language narrative (structured documentation). It extends the idea of literate programming into the modeling domain, unifying structure (model) and narrative (structured text) into a single intelligible artifact interpretable by both humans and AI. The concept of “Literate Modeling” has been explored previously by some researchers and developers, mostly as an approach to improve documentation or code comprehension. However, those attempts did not establish a systematic modeling methodology that integrates models, narrative, and AI assistance as a unified framework. The Literate Model is a modeling concept newly systematized and proposed by SimpleModeling for the AI era. Building upon the ideas of literate modeling, it redefines them as an intelligent modeling foundation that enables AI-collaborative knowledge circulation and model generation. It is not merely a modeling technique but a framework that embeds human reasoning and design intent as narrative within the model, enabling AI to analyze and reconstruct them to assist in design and generation.
- CML (Cozy Modeling Language)
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CML is a literate modeling language for describing Cozy models. It is designed as a domain-specific language (DSL) that forms the core of analysis modeling in SimpleModeling. CML allows model elements and their relationships to be described in a narrative style close to natural language, ensuring strong compatibility with AI support and automated generation. Literate models written in CML function as intermediate representations that can be transformed into design models, program code, or technical documentation.
- Cloud Native Component Framework (CNCF)
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Cloud Native Component Framework (CNCF) is a framework for executing cloud application components using a single, consistent execution model. Centered on the structure of Component, Service, and Operation, it enables the same Operation to be reused across different execution forms such as command, server (REST / OpenAPI), client, and script. By centralizing quality attributes required for cloud applications—such as logging, error handling, configuration, and deployment—within the framework, components can focus on implementing domain logic. CNCF is designed as an execution foundation for literate model-driven development and AI-assisted development, separating what is executed from how it is invoked.