01
The Module Boundary System
Structure a large project into feature modules with enforced boundaries. Keep your agent logic decoupled from your web framework and infrastructure so your codebase stays navigable at 100k+ lines.
// PRODUCTION-GRADE PYTHON
Stop Your AI Flows From Becoming an Unmaintainable Python Mess.
The enterprise architecture blueprint for senior engineers. Stop gluing LangChain to FastAPI and praying it holds at scale. Learn how to apply proper Dependency Injection, Domain-Driven Design, and Event-Driven Sagas to build AI orchestration that actually survives production.
Get the Blueprint
For devs, tech leads and architects. Not for "just use modules, bro" people.
// THE PROBLEM
You Already Know How The 'AI Demo' Ends
You know how to build enterprise software in Java, Spring, or .NET. But now you're tasked with building AI agent workflows, and the entire ML ecosystem lives in Python. The problem? Every tutorial is a Jupyter notebook or a single main.py file. When you try to put it in production, the cracks show instantly. State management is a joke. Testing is impossible without burning OpenAI credits. And the second your team grows, they reintroduce the exact spaghetti code you've spent your career learning to avoid.
"The orchestration is a FastAPI app with a bunch of if/else and try/except blocks, and it's becoming unmaintainable. Adding a new agent step means touching spaghetti code every time."
// THE BLUEPRINT
What You'll Build and Master
02
Dependency Injection That Scales
Real DI with Dishka. Build testable interfaces so you can swap LLM providers instantly and run unit tests without hitting live API endpoints. No more Depends() spaghetti.
03
Idiomatic DDD & CQRS
Aggregates, entities, and value objects that read like idiomatic Python, not a Java cosplay. Learn how to manage complex agent state and memory without letting your database leak into your prompt logic.
04
Event-Driven Foundations That Won't Betray You
Agent steps will fail. LLMs will hallucinate. Learn how to use the Outbox pattern and Sagas (with Temporal/FastStream) to ensure distributed agent workflows can recover cleanly without corrupting state.
// IS_FOR = TRUE
Who It's For
- Senior devs, tech leads, or architects managing real teams.
- Engineers migrating from Java/Spring or C# looking for a mature standard.
- Teams building complex AI agent orchestration that needs enterprise rigor.
// IS_FOR = FALSE
Who It's NOT For
- Weekend CRUD apps or beginner side projects.
- Developers who believe "you don't need DI in Python."
- People looking for a 20-minute LangChain wrapper tutorial.
// FAQ
Objections, handled.
// GIT_BLAME
Built by someone who's actually scaling this in prod.
Nick Goupinets
Senior Software Engineer, 20+ years.
I spent two decades in Java/Spring enterprise systems-the kind with real compliance, real transaction volume, and real consequences. So when I say "no Java cosplay," it's because I know exactly which enterprise patterns to steal and which to leave rotting in a FactoryFactoryBean.
I've shipped production LLM agents that cut error triage from hours to minutes, run Temporal in anger, and migrated legacy monoliths to event-driven microservices-without the luxury of downtime windows.
I'm not a PowerPoint architect. I build productions systems daily, and I'm not afraid to read the stack traces.
// credentials
AWS Solutions Architect · AWS ML Specialty · SEI Software Architecture Professional · NVIDIA GenAI (NCA-GENL) · Core contributor to HAPI FHIR
// JOIN_WAITLIST
Stop reinventing the architecture.
Enter your email to get early access and a heavy pre-launch discount.
What you get: A production-grade reference solution you can clone - plus the reasoning behind every architectural decision. A tool, not a 40-hour video course you'll never finish.