The transition from Data Scientist to AI Architect wasn’t a promotion; it was a shift in perspective. As a Data Scientist, I obsessed over accuracy, F1 scores, and hyperparameter tuning. Success meant a model that performed well on a test set.
But as I moved into architecture—working on tailored cloud AI platforms and governing deployment lifecycles—the definition of success changed. It wasn’t about whether the model could work; it was about whether the system would survive.
This blog creates a space to explore that difference. It is about the “boring” engineering reality that sits underneath the gleaming surface of Generative AI.
Key Takeaways
- AI Architecture bridges the gap between experimental code and production systems.
- Success depends less on model choice and more on governance, observability, and patterns.
- This blog helps structure the chaos through three lenses: Principles, Patterns, and Diagrams.
The Architect’s View
I started my career as a Software Developer before moving into Data Science, and eventually, Architecture. That trajectory shaped my view of Artificial Intelligence. I don’t see AI as magic; I see it as a probabilistic software component that fails in interesting, often terrifying, ways.
In my work at Beazley Group, building platforms for governance and lifecycle tracking, I realised the industry is saturated with tutorials on how to fine-tune an LLM, but starved of guidance on how to govern, monitor, and integrate it safely.
There is a gap between the “Hello World” of a Jupyter notebook and the “Day 2” reality of a regulated enterprise environment. This blog is my attempt to fill it.
What to Expect
I am organising my writing around three specific streams, designed to break down the complexities of AI systems into manageable pieces.
1. Principles
We need to agree on the rules before we play the game. This stream covers the foundational thinking required to build responsible AI systems.
- Governance: How do we ensure compliance without stifling innovation?
- Observability: How do we debug systems that are non-deterministic by nature?
- Ethics: moving beyond platitudes to implement responsible AI checks in CI/CD pipelines.
2. Patterns
Architecture is the art of not reinventing the wheel. Here, I’ll document reusable solutions to common problems.
- RAG Architectures: Going beyond naive vector search to hybrid retrieval and re-ranking.
- Agent Orchestration: Managing state and unexpected behaviours in autonomous systems.
- Integration: connecting probabilistic models to deterministic legacy systems.
3. Diagram Clinic
A picture is worth a thousand tokens. One of my frustrations with current technical literature is the ambiguity of architecture diagrams. Boxes labelled “AI” connected by arrows labelled “Data” are not helpful. In the Diagram Clinic, we will:
- Dissect common architectural diagrams.
- Critique visual abstractions.
- Redraw confusing systems using standard notations (like Mermaid) to clarify exactly what runs where.
A Note on Style
You will find no hype here. The goal is to be professional but personable, and above all, pragmatic. I prefer clear definitions over buzzwords, and I value the “how” as much as the “what”.
If you are building AI systems that need to last longer than the current hype cycle, you are in the right place.