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Long-form teardowns of how production AI systems stay governable: idempotency, decision lineage, context budgets, statistical process control, guardrail planes and landing zones. Each essay names the data structure, the failure mode and the control.
6 essays · 12 topics · 48 minutes of reading
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Model endpoints belong in the same governed landing zone as any workload: private connectivity, preventive policy, a central gateway with keyless auth, and immutable logs.
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Newest first. Code samples are illustrative and deliberately compact; the patterns are the ones we ship.
A system prompt instruction is not a control. Guardrails belong in an enforcement plane outside the model: classifiers, policy-as-code on tool calls, fail-closed validators.
Apply statistical process control to AI defect rates: operational defect definitions, p-charts with honest denominators, and run rules that catch drift before thresholds do.
Treat the context window as a budget: per-slot token caps, priorities and eviction rules, content-hash dedup and provenance tags for every chunk the model sees.
Logs and traces are not lineage. Lineage is a hash-chained DAG of every input behind a decision, captured at decision time so it can be replayed and defended.
Idempotency keys give every AI-driven action a stable identity bound to its inputs, so decisions can be counted, replayed and audited instead of reconstructed.
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