Turn AI research into a defensible decision process with qualified sources, claim-level evidence, explicit scope, and visible uncertainty. Preserve conflicting findings and connect recommendations to the evidence that supports them.
Turn AI-assisted engineering into a reviewable delivery contract. Define requirements, interfaces, authorization, failure behavior, testing, operations, and evidence before claiming an implementation is complete.
Assess enterprise AI through a traceable chain of system boundaries, threats, controls, evidence, residual risk, and approval. Distinguish proposed controls from tested behavior and reassess material changes.
Start AI use case evaluation with a measurable workflow problem. Compare simpler alternatives, apply hard gates, model complete costs, and design a bounded pilot that can disprove the investment thesis.
Multiple AI reviewers can share the same mistake. Design assurance quorums with isolated judgments, protected evidence, deterministic vetoes, and measured marginal value.
Govern dynamic model routing without allowing fallback to inherit production authority. Record resolved models, qualify route members by action, and define when degraded paths must require approval or hold execution.
A model upgrade can change what an AI agent does with existing permissions. Requalify its action boundaries, compare tool behavior, test independent controls, and expand production autonomy only when the evidence supports it.
Restore agent authority through action-level requalification, measured canary exposure, and enforceable promotion and demotion rules. Use current evidence to decide which autonomy levels can safely return.
Choose reversal, compensation, forward recovery, or containment according to the observed effect. Authorize corrective actions, preserve concurrent changes, and track consequences that cannot be undone.
Reconcile uncertain AI agent actions using target evidence, protected intent, current authority, and idempotency. Keep unresolved effects from becoming duplicate or unauthorized work after recovery.
Restore AI agent data under a current recovery authority. Reconcile approvals, claims, credentials, and target effects before admitting bounded successor execution.
Separate work ownership from receiver-enforced authority. Use a local fencing lab to examine stale workers, current grants, duplicate handling, and the limits of restored control state.
Use a single-host SQLite lab to examine durable approval claims, worker restarts, and uncertain execution. Preserve ownership and reconcile effects before retrying or trusting restored state.
Bind approval to the exact Kubernetes request, object identity, starting state, executor, and validity window. Use conditional patches while keeping authorization, approval consumption, and uncertain outcomes separately governed.
Prepare a Recursive Trust Benchmark reviewer pilot with isolated inputs, frozen trial assignments, strict response validation, and complete accounting of valid, invalid, and missing results.
A proposed Recursive Trust Benchmark separates reviewer judgment, control testing, and workflow outcomes to assess detection, prevention, evidence, and useful task completion.
Restore an AI agent’s accepted behavior and current controls before restoring authority. Quarantine suspect memory, requalify evaluators, preserve evidence, and reconcile external actions.
Human oversight needs qualified reviewers, independent evidence, usable stop controls, and enough time to act. Test practical readiness and preserve a working fallback before granting agents authority.
Design independent agent assurance on Azure Local and hybrid cloud. Separate workload continuity from permission, bound local execution, and verify recovery after reconnection.
Apply independent agent assurance to VMware Cloud Foundation 9.1.1. Separate agent workloads from authorization, scoped execution, verification, and evidence.
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