Good piece, Daniel. The business context framing is what separates useful architecture writing from vendor-sponsored blueprint recaps.
Six solid principles. I'd argue there's a seventh missing: Programmability. Not just API access -- the full developer experience. How easy is it to build on, test, and extend the platform over time? How quickly does a new engineer reach productive output?
You actually set this up yourself when you mention that Senior Engineers are now expected to build and maintain platforms from scratch. That context makes the question urgent. A platform that's scalable and resilient but painful to extend doesn't have a technical bottleneck -- it has a human one. Which is harder to fix and rarely shows up in architecture diagrams.
Foundations hurt most when they're wrong and you can't feel it until everything depends on them. I built a custom FastAPI/SQLite kanban for my AI agent over 2 months, 54 commits, real-time updates, deep automation wiring.
Then it became the foundation 50+ scripts depended on, and every schema change had ripple effects. Moved to Fizzy, an open-source Rails board from 37signals. The 3,700-line API client shrank to 94 lines. The agent ran without interruption. Borrowing a proven foundation is almost always right when the value is in what sits above it. That's the full story: https://thoughts.jock.pl/p/wizboard-fizzy-ai-agent-interface-pivot-2026
Good architecture is about building around what the business actually needs
Good piece, Daniel. The business context framing is what separates useful architecture writing from vendor-sponsored blueprint recaps.
Six solid principles. I'd argue there's a seventh missing: Programmability. Not just API access -- the full developer experience. How easy is it to build on, test, and extend the platform over time? How quickly does a new engineer reach productive output?
You actually set this up yourself when you mention that Senior Engineers are now expected to build and maintain platforms from scratch. That context makes the question urgent. A platform that's scalable and resilient but painful to extend doesn't have a technical bottleneck -- it has a human one. Which is harder to fix and rarely shows up in architecture diagrams.
Looking forward to the next parts.
Foundations hurt most when they're wrong and you can't feel it until everything depends on them. I built a custom FastAPI/SQLite kanban for my AI agent over 2 months, 54 commits, real-time updates, deep automation wiring.
Then it became the foundation 50+ scripts depended on, and every schema change had ripple effects. Moved to Fizzy, an open-source Rails board from 37signals. The 3,700-line API client shrank to 94 lines. The agent ran without interruption. Borrowing a proven foundation is almost always right when the value is in what sits above it. That's the full story: https://thoughts.jock.pl/p/wizboard-fizzy-ai-agent-interface-pivot-2026