Three patterns that make AI agents production-safe
Content firewalls, evaluation harnesses, and the kill-switch — the discipline behind 99.99% AI uptime.
Production AI is different from notebook AI
A model that scores 92% on a benchmark notebook can fail spectacularly on a Friday afternoon when a customer phrases a query in a way the test set never considered. The discipline that bridges the gap is operational, not architectural.
Pattern 1 — Content firewalls
Every input passes through an input filter and every output through an output filter, both with explicit allow-lists for the tenant context. Prompt injection is not a vibes problem; it is a parsing problem with parsing solutions.
Pattern 2 — Evaluation harnesses
Run a representative golden-set against every model deployment, with both quantitative and qualitative scoring. Fail-closed on any regression beyond a two-sigma drift. Make this CI for prompts — the way it already is for code.
Pattern 3 — The kill-switch
Each agent has a kill-switch that takes it out of production traffic in under five seconds. The switch is exercised in tabletop drills monthly. We have used it twice in production; both times we caught the failure before customers did.
The compounding effect
On their own these are obvious. The compounding effect of running all three from day one is what gets you to 99.99% AI uptime in regulated environments — and what regulators will accept as evidence of a responsible AI operating model.
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