Ablely Manages AI Agent Workload Lifecycle with Amazon EKS-Based Agent Sandbox

Details

Ablely shared an operational case study on managing the lifecycle of per-project workloads for AI Builder using Agent Sandbox on Amazon EKS
Chose Agent Sandbox over standard Kubernetes workloads to declare execution specs, state, and expiration as a single resource and delegate lifecycle management to a controller
Applied a workflow that bundles workload code into Git bundles and stores them in Amazon S3 to preserve artifacts during Sandbox interruptions and restore them upon resumption
Established an operational policy combining Karpenter's do-not-disrupt annotation and Disruption budget to protect running workloads while reclaiming idle nodes
Set requests to 500m CPU and 2Gi memory to account for resumption-phase load, and applied the MostAllocated scheduler to improve resource efficiency
Plans to strengthen isolation with microVM runtimes such as Kata Containers and build a common internal execution foundation

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