AI-Assisted Domain-driven Design and business models archetypes

Modern AI-assisted development clearly shows that simple “vibe coding” does not work for projects whose complexity extends beyond prototypes and toy applications.

Generative AI can produce code extremely quickly, but it only works reliably inside an environment that is understandable and constrained — built from well-defined modules, clear domain boundaries, and a sound architecture.

This is why certain engineering skills are now essential for using AI as a real productivity multiplier:

• Modularization, enabling delegation to agents in small, unambiguous contexts.
• Verification and critical evaluation of AI output based on engineering best practices.
• Designing autonomous models, APIs, and test layers that define stable boundaries for AI.
• Knowledge of business domain archetypes, allowing you to assemble systems like building blocks and prototype new solutions extremely quickly, with heavy help from generative AI.

AI (for now) does not think modularly, does not design architecture, and does not preserve systemic coherence. Its power emerges only when a human intentionally reduces context and defines the constraints within which an agent can operate. The engineer’s role is to plan structure, define boundaries, specify contracts — and only then delegate implementation. In this model, AI becomes a massive productivity accelerator.

Throughout the course we use tools such as Claude Code to create precise system-level instructions and control AI through specifications rather than one-off prompts. Participants learn to build their own agent-driven workflows for module implementation, testing, domain analysis, and code review.

On the technical side, code generation is grounded in well-known tactical patterns and local layered architectures, ports and adapters, separation of concerns, and modularization techniques — creating an ideal environment for controlled use of AI. On the business side, participants work with proven domain archetypes such as Product, Pricing, Accounting/Billing, Party, and graph-based models. These archetypes create a shared “business language” through which AI can effectively collaborate with engineers and enable prototyping of real systems in hours, not months.

The training focuses on the practical design of systems that are both AI-friendly and agent-friendly. It teaches modern engineering practices that allow AI to be used effectively, safely, and deliberately across large parts of the software development lifecycle — from domain exploration, through modular architecture and autonomous model implementation, to testing, verification, and rapid prototyping through business archetypes and agent workflows.

This combination of technical patterns, business archetypes, spec-driven development, and advanced Claude agents transforms AI from a curiosity into a production-ready engineering tool with real power.