We don’t trust AI models.
We trust the pipeline around them.
Point a model at a problem and it writes plausible code — code that compiles, demos, and is quietly wrong. For software that sends trades to the market or performs pre-trade risk checks, that's the most expensive failure mode there is. So we don't rely on the model. We rely on a pipeline built around it.
- 01
Mechanical gates the model can’t argue with
Hooks that return an error a layer violation, an oversized file or an off-convention migration. Judgment is removed from the things that should never be judgment calls.
- 02
A forced process
Plan-first, test-first, one concern at a time. The design is a two-page contract written before any code, and tests are sacred.
- 03
Adversarial review
Independent agents that trace every ripple effect and are told not to be polite. Any medium-or-worse finding blocks the change.
- 04
A human at the top of the funnel
What reaches the human is distilled — a short design and a ranked findings list, so judgment is spent where machines are weakest: architecture and business correctness.
- 05
A learning loop
Every human correction is distilled into a permanent rule the planner applies on the next task. The system gets harder to fool over time.
AI is a force multiplier that must be kept on a leash. Unsupervised, it is a sure path to unmanageable technical debt. Constrained, reviewed, and with a feedback loop, it improves productivity and quality — while a human keeps final authority over all critical processes.
We write about how this works in practice — the gates, the bugs, the trade-offs — in Quietly Wrong
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