AI & ML

TL;DR A Kubernetes admission rule can establish that a proposed value is permitted without establishing that this particular action is currently approved. Bind approval to the exact operation, target identity, relevant starting state, initiating authority, execution identity, and validity window. Keep approval consumption and uncertain execution in a protected record outside the agent. This companion […]

TL;DR Testing AI agent authorization on Kubernetes requires more than a successful policy function or an impersonated permission check. Use actual workload credentials, distinguish native authorization from admission validation, and read the resulting state through a separately permissioned identity. A request rejected because the client cannot authenticate is not evidence that an action-specific restriction worked.

US president has resisted calls for regulation but said he would appoint a ‘tsar’ to monitor for ‘bad’ behaviour

TL;DR AI agent execution gate testing should establish whether a prohibited action remains blocked when the reviewer gets the decision wrong. Submit the proposed operation through the control path, examine the actual effect, and keep authorization, execution, and verification outcomes separate. This companion introduces a sixteen-scenario offline lab for testing that distinction. It preserves the

TL;DR Start a Recursive Trust Benchmark pilot by proving the measurement path before comparing reviewers. Validate the source cases, keep the answer key outside the candidate environment, freeze the trial assignments, and retain responses without silently repairing them. Account for every planned trial, including invalid and missing results. The original sixty-case starter supports decision-case development,

TL;DR The Recursive Trust Benchmark is a proposed method for comparing whether different assurance designs detect incorrect proposals, prevent prohibited effects, and produce independently supportable completion evidence. It separates reviewer comparison, direct control testing, and end-to-end agent evaluation so that improvements in one are not misrepresented as improvements in another. Version 0.1 includes a downloadable

TL;DR AI agent disaster recovery must address the possibility that the model, memory, policy, evaluator, or evidence is unreliable even while the infrastructure remains healthy. Restoring a runtime and reconnecting its previous state can recreate the failure. Recovery must establish an independently defensible operating baseline before execution authority returns. Recover different kinds of state differently.

TL;DR Human oversight of AI agents is useful only when people can identify material errors, obtain evidence outside the agent’s account, and intervene before the consequences exceed the approved boundary. An approval record proves that someone made a decision. It does not establish that the reviewer had the knowledge, information, time, or authority required to

TL;DR Independent agent assurance on Azure Local requires separate answers to three questions: can the workload continue, can the agent still obtain or exercise authority, and can an independent mechanism verify what happened? A functioning local application, cached gateway configuration, or valid credential does not answer all three. Design the action boundary for connected operation,

TL;DR Independent agent assurance on VMware Cloud Foundation (VCF) 9.1.1 requires more than separate tenants, protected model endpoints, and healthy infrastructure. The agent must remain unable to administer the controls that authorize its actions, obtain the executor’s credentials, or rewrite the evidence used to accept the result. Map those requirements across VMware vSphere Kubernetes Service