The Origin Story
It started with a distributed systems problem that wouldn't behave. Years ago, working on a platform that needed to serve millions of requests per day with near-zero latency, I realised that the interesting part wasn't the technology itself — it was the gap between what the system did and what the humans using it actually needed.
"Good architecture makes the right thing easy and the wrong thing hard. Great architecture makes people not notice the architecture at all."
When machine learning started reshaping what systems could do, I saw the same pattern: the models were impressive, but the real leverage was in how they were deployed, integrated, and governed. How they fit into the organisations and workflows they were meant to serve.
That's what led me to AI/ML architecture — not just building the models or running the infrastructure, but designing the full system: the data pipelines, the serving layer, the human interfaces, the governance frameworks, and the feedback loops that make everything improve over time.
Now with agentic AI, we're at another inflection point. The question isn't whether LLMs are capable — it's whether we can build the scaffolding that lets them act reliably, safely, and usefully at enterprise scale. That's the problem I work on every day.