Abstract

This publication discloses, with enabling detail, a Kubernetes-native control-plane pattern for operating fleets of long-lived, autonomous AI personas as declarative infrastructure. Each persona's cognitive lifecycle — the enablement of its cognitive loop, the interval of that loop, its per-day and per-request token budgets, its granted tool set, and the business domains it may act within — is expressed in the spec of an AIOSPersona Custom Resource. A dedicated operator continuously reconciles observed runtime signals into the resource's status subresource, which carries a five-state lifecycle machine — Dormant → Booting → Active → Dreaming → Suspended — together with two live counters: attentionQueue (the persona's pending cognitive work depth) and tokensUsedToday (the persona's day-scoped token consumption). Two companion resources, AIOSDomain and AIOSAgent, declare enforcement-policy blocks (token ceilings, duration caps, edit caps, sandbox, auto-rollback) that the same operator projects onto Kubernetes primitives. The distinguishing data-plane mechanism is that gateway replicas autoscale on the depth of a persisted cognitive work queue — the count of items in a cognitivequeue table awaiting a cognitive cycle — rather than on HTTP request rate, CPU utilization, or memory pressure. The disclosure establishes dated public prior art over three points no existing Kubernetes AI operator combines: (a) an idle Dreaming memory-consolidation phase modeled as first-class reconciled status; (b) token-budget exhaustion expressed as a declarative transition into a Suspended lifecycle state; and (c) cognitive-queue depth as the horizontal-autoscaling trigger. A clean-room, dependency-free, offline-runnable Node.js reference implementation accompanies the text.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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