Building AI-Ready Systems Without Fear of Framework Obsolescence
The GenAI landscape evolves rapidly. Model Context Protocol just arrived. New orchestration tools emerge constantly. In 6 months, something else will reshape the field.
This pace creates two problems:
For teams still deciding: Which orchestrator to choose? LangGraph? Temporal? Airflow? Something new? Investing months in the wrong tool means wasted time and resources. Result: paralysis.
For teams already committed: Deep integration with one tool creates coupling. When the next evolution arrives, migration becomes prohibitively expensive. Result: lock-in.
This document presents a GenAI-Friendly Architecture Pattern that solves both problems.
The pattern enables you to:
- ✅ Build working systems today without committing to any orchestrator
- ✅ Maintain maximum flexibility to adopt any AI orchestration tool later
- ✅ Migrate between tools without rewriting business logic
- ✅ Experiment freely without fear of wrong choices
Structure your business logic as pure functions with standardized interfaces. These functions work standalone today and integrate seamlessly with any orchestration platform tomorrow.
def validate_order(state: OrderWorkflowState) -> OrderWorkflowState:
"""Validate order according to business rules."""
validator = get_order_validator(state["order_id"])
validator.execute_validations()
state["validation_result"] = validator.get_summary()
state["current_step"] = "validated"
return stateThe same function works with:
- Direct execution (no framework)
- FastAPI
- LangGraph
- Apache Airflow
- Temporal
- Prefect
- Model Context Protocol (MCP)
- Any future orchestration tool
| Benefit | Description |
|---|---|
| Immediate Start | Build functional systems today without choosing an orchestrator |
| Zero Lock-in | Switch orchestrators without touching business logic |
| Risk-Free Experimentation | Try different tools without commitment |
| Future-Proof | Ready for whatever comes next in AI evolution |
| Business Focus | Functions map to business capabilities, not technical abstractions |
| Progressive Enhancement | Add orchestration when needed, not before |
Beyond flexibility, this pattern offers unexpected visibility: revealing what your system actually does vs. what you think it does.
When you structure code as business functions, reality becomes visible. A 50,000-line codebase might reveal only 2 fully implemented business capabilities out of 12 identified. That transparency is powerful.
📄 GenAI Friendly Architecture Pattern by Hugues Dtankouo.pdf
Complete guide covering:
- Why this pattern matters now
- Concrete implementation examples
- Integration with LangGraph, Airflow, Temporal, MCP
- Business coverage revelation technique
- Progressive enhancement approach
📄 Pattern Architectural GenAI Friendly par Hugues Dtankouo.pdf
Guide complet en français avec le même contenu.
Today: Direct execution
state = OrderWorkflowState(order_id="12345", ...)
state = validate_order(state)
state = calculate_pricing(state)
state = approve_order(state)Tomorrow: With any orchestrator
# LangGraph
graph = StateGraph(OrderWorkflowState)
graph.add_node("validate", validate_order)
graph.add_node("price", calculate_pricing)
graph.add_node("approve", approve_order)Same functions. Different orchestration. Zero refactoring.
This pattern is for teams who:
- Are paralyzed by orchestrator choice
- Are locked into a framework that might not last
- Want to build AI systems without framework commitment
- Need flexibility as technology evolves
- Value business logic that survives technological change
- Identify Business Capabilities - What should your system do?
- Define State - What data flows between steps?
- Create Functions - One per business capability (keep them simple, 10-30 lines)
- Delegate Complexity - Extract to business classes when needed
- Start Simple - Direct calls first, add orchestration later
Functions orchestrate, they don't implement. Keep workflow functions clean (10-30 lines). Put complexity in business classes where it belongs.
Hugues Dtankouo Senior Python Developer - Gen AI
7 years of experience solving complex automation challenges in investment banking and capital markets through sophisticated orchestration of cutting-edge generative AI.
Expertise spans highly regulated environments in investment banking, energy sector, Big Four consulting, and insurance. Specializes in building production-ready AI architectures that transform strategic challenges into innovative solutions.
This work is licensed under the MIT License - see the LICENSE file for details.
November 1, 2025
Whether you're paralyzed by choice or locked in a tool, there's a path forward.