Unchecked versioning in object storage accumulates silently: delete markers, non-current versions, and stale objects that no lifecycle rule is cleaning up. At scale, manual review is impractical and broad removal scripts become dangerous. This webinar, presented by a MinIO curriculum engineer, introduces a structured AI-augmented workflow for safe storage reclamation in AIStor. The session covers how to inventory versioned buckets using MC CLI commands, how to aggregate and filter raw output using jq before passing it to an AI agent, and how to generate per-prefix cleanup plans with differentiated thresholds rather than bucket-wide sweeps. The core plan-confirm-act methodology keeps the operator in control of all destructive commands. The webinar also covers configuring ILM rules to prevent reaccumulation and when to use AIStor Inventory instead of MCLS for production-scale environments.
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Feeding raw MCLS output directly to an AI agent at scale degrades reasoning quality and creates false confidence; aggregating and filtering data to the right grain before the agent sees it is the critical step that separates safe cleanup from risky guesswork.
In a live demo, a 30-minute pipeline ingest script produced 255 objects and almost 20,000 versions, illustrating how quickly unchecked versioning accumulates and why broad-threshold removal scripts fail when prefix semantics differ across a bucket.
AIStor Inventory runs server-side batch reporting on buckets and prefixes, including per-object version counts and total sizes, making it the correct tool for production environments where client-managed listing operations against millions of objects are impractical.
AIStor administrators and storage engineers dealing with versioning buildup or uncontrolled storage growth, and anyone evaluating how to safely incorporate AI agents into operational storage management workflows without introducing deletion risk.
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