Blog
Practical essays on monitoring, drift, trust, routing, cost, and the signals that matter once AI systems move past the demo.
"Healthy" is a claim, not a fact. The most dangerous AI systems in production are the ones whose definition of healthy was inherited, never challenged, and quietly never updated.
Read articleMost teams shipping generative systems do not lack tooling. They lack an on-call rotation, an ownership matrix, and a runbook for behavioural anomalies — the operational scaffolding every other production system gets by default.
Read articlePrompt edits alter system behaviour, often in ways no one watching infrastructure can see. They deserve the versioning, telemetry expectations, rollout criteria, and rollback paths every other production change gets.
Read articleWhy token counts, latency charts, and model usage graphs often say very little unless they are connected to user outcomes and product semantics.
Read articleWhy the hardest AI incidents are rarely outages. They are the systems that keep responding, keep looking healthy, and quietly stop being useful.
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