翻訳者への注意：以下の内容は現在英語です。文脈と意味を維持しながら、日本語に翻訳してください。
Note to translators: The following content is currently in English. Please translate it to Japanese, maintaining the context and meaning.
The ONDEMANDENV Platform
This is the **Semantic Projection Engine**—the practical, end-to-end workflow that transforms your living semantic models into running, evolvable, and verifiable systems.
From Abstract to Concrete: The Core Components
A philosophy is only as good as its implementation. ONDEMANDENV provides the concrete tools and concepts to make semantic engineering a reality. These are the building blocks of the projection engine.
1. The Contracts Library (`contractsLib`): The Semantic Anchor
The contractsLib is the heart of the system. It's where the abstract logic of your semantic model is first codified into a machine-readable format. It's a version-controlled TypeScript library that defines the behavioral guarantees and data contracts between all components of your system.
- The Single Source of Architectural Truth: It transforms architectural knowledge from diagrams and documents into version-controlled, executable assets.
- Proactive Governance: It acts as a "forcing function" that prevents integration failures, security gaps, and configuration drift before they happen.
- Enables Collective Learning: Changes to the `contractsLib` via Pull Requests create a shared, evolving understanding of the system's architecture across all teams.
2. Application-Centric Infrastructure: Projecting Boundaries
ONDEMANDENV rejects the traditional, layered approach to infrastructure. Instead, it uses an Application-Centric model that projects the boundaries defined in `contractsLib` into a complete, vertical slice of business functionality.
- Cohesive Bounded Contexts: A service's entire stack—containers, databases, security policies, and networking—is managed as a single, atomic unit.
- Aligned with Business Value: Ownership aligns with business capabilities, not technology layers, empowering teams to innovate within their domain.
- The Ideal Foundation for AI: These well-defined boundaries provide the perfect guardrails for AI-assisted development, preventing architectural decay.
3. Enver (Environment Version): The Unit of Evolution
An Enver is an isolated, versioned, service-level deployment. It is the concrete, running manifestation of a specific version of a service's Bounded Context. It is the unit of evolution and selection.
- Fearless Experimentation: Each Git branch can have its own `Enver`, allowing developers to innovate and test in complete isolation without fear of breaking shared environments.
- Evolutionary Selection: Envers are candidates for survival. Successful ones are promoted to become new stable baselines, while others are discarded. This creates an evolutionary model where only the best adaptations thrive.
- Capturing Collective Intelligence: Each Enver locks in a set of validated dependency versions, creating a "known-good" snapshot of the ecosystem's collective intelligence.
4. Constellation: The Verifiable System
A Constellation is the emergent, end-to-end representation of your entire distributed system. It is formed by connecting individual `Envers` together through their shared understanding of the `contractsLib`.
- Dynamic System View: It provides a living, breathing view of your architecture, composed of specific, versioned components.
- Fearless End-to-End Testing: Within its Constellation, developers—human or AI—can perform true end-to-end validation without any risk of system-wide disruption.
- Validating the Model: The ultimate test of the semantic model's integrity happens here, where the projected system is validated as a whole.
Patterns in Practice
These components enable powerful patterns for solving real-world distributed systems challenges.
Pattern: Isolated Full SDLC Environments per Branch
Problem: Shared dev/QA environments create bottlenecks, contention, and configuration drift, preventing true parallel development of complex features.
ONDEMANDENV Solution: With a single commit message (`odmd: create@dev`), a developer can provision a complete, high-fidelity SDLC clone for their feature branch. This `Enver` is a fully isolated Bounded Context, enabling deep, meaningful branching strategies and fearless testing.
Pattern: AI-Assisted Development within Bounded Contexts
Problem: AI code generators, lacking architectural context, often produce code that violates domain boundaries and creates accidental complexity.
ONDEMANDENV Solution: The `contractsLib` provides the perfect specification to guide AI. By giving the AI the architectural context of the `Enver` and its `Product`/`Consumer` contracts, it can generate code that is not only functionally correct but also architecturally compliant, maintainable, and aligned with DDD principles.
Visualizing the Platform
These abstract concepts are best understood visually. Explore these diagrams to see how the components and patterns fit together.