Before the Pod Exists: How Kueue Decides Whether an AI Job May Run
Learn how Kueue admits Kubernetes AI and batch jobs, manages scarce GPU quota, and hands workloads to kube-scheduler.
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Learn how Kueue admits Kubernetes AI and batch jobs, manages scarce GPU quota, and hands workloads to kube-scheduler.
Read noteEvaluation connects product requirements, versioned datasets, component failures, operational constraints, release gates, and production feedback.
Read noteLong-running AI work should be modeled as durable state transitions with idempotent stages, checkpoints, bounded retries, cancellation, and backpressure.
Read noteSchema-constrained generation is only the first trust boundary. Production systems still need evidence, policy, authorization, bounded repair, and explicit disposition.
Read noteA model gateway should centralize provider compatibility, routing, quotas, and data policy without absorbing product workflow logic.
Read noteA production AI feature is not a model call. It is a distributed system with explicit boundaries for authorization, orchestration, validation, durable execution, and evaluation.
Read noteUnderstand how paused agent requests trade accelerator memory against KV-cache transfer, recomputation, resume latency, and system goodput.
Read noteBuild an execution-based grader that separates final-state success from tool-selection, argument, authorization, ordering, and duplicate-work failures.
Read noteUnderstand idempotency as a semantic contract for ambiguous tool outcomes, then prove durable replay and conflict handling locally.
Read noteA practical field guide to the capabilities, protocols, risks, and minimum path for building a first useful AI agent in 2026.
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