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.
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.
Move from an offline simulation to a disposable Kubernetes cluster. Test separate agent, executor, and observer identities, resource-scoped permissions, admission rules, and independent state readback.
Test AI agent execution controls with a sixteen-scenario offline lab. Separate authorization, target effects, and completion evidence before validating a real platform.
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.
Rehearse agent behavior in controlled environments before granting production authority. Test realistic failures, policy conflicts, recovery, and repeated trials against the scope the agent will actually receive.
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