AI Agent Evaluation: A Passing Test Is Not Production Proof
Evaluate the complete agent release and its enforcement boundaries. Use fault injection, independently checked outcomes, and explicit limits when introducing production authority.
Agentic AI
AI Agent Authorization: Approval Is Not a Blank Check
Bind agent approval to the exact action, identity, target, policy, and validity window. Revalidate changed conditions and give uncertain execution and recovery their own authority rules.
Security Review Is Not a Checklist: An Evidence Driven Assessment Model for Enterprise AI
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.
When Two AI Reviewers Agree: Building Assurance Quorums Without False Independence
Multiple AI reviewers can share the same mistake. Design assurance quorums with isolated judgments, protected evidence, deterministic vetoes, and measured marginal value.
When the Router Chooses the Model: Governing Fallback Authority for AI Agents
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.
AI Agent Compensation: Why Rollback Is Not Undo
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 Before You Retry: Recovering Uncertain AI Agent Actions
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.
Restoring State Must Not Restore Authority: Independent Recovery Admission for AI Agents
Restore AI agent data under a current recovery authority. Reconcile approvals, claims, credentials, and target effects before admitting bounded successor execution.
Fencing Stale AI Workers: Enforcing Authority at the Receiver
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.
Building an AI Agent Execution Ledger That Survives Restarts
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.
Binding AI Agent Approvals to Kubernetes Changes
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.
From Simulation to Kubernetes: Testing Agent Identity and Admission
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.
Testing the AI Agent Execution Gate: From Review Scores to Control Evidence
Test AI agent execution controls with a sixteen-scenario offline lab. Separate authorization, target effects, and completion evidence before validating a real platform.
Running the Recursive Trust Benchmark: Your First Reviewer Pilot
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.
The Recursive Trust Benchmark: Test AI Assurance
A proposed Recursive Trust Benchmark separates reviewer judgment, control testing, and workflow outcomes to assess detection, prevention, evidence, and useful task completion.
AI Agent Disaster Recovery: Restore Trust Before Authority
Restore an AI agent’s accepted behavior and current controls before restoring authority. Quarantine suspect memory, requalify evaluators, preserve evidence, and reconcile external actions.
When the Humans Can No Longer Check the Machine
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.
Independent Agent Assurance on Azure Local and Hybrid Cloud
Design independent agent assurance on Azure Local and hybrid cloud. Separate workload continuity from permission, bound local execution, and verify recovery after reconnection.
Independent Agent Assurance on VMware Cloud Foundation 9.1.1
Apply independent agent assurance to VMware Cloud Foundation 9.1.1. Separate agent workloads from authorization, scoped execution, verification, and evidence.
The Architecture That Keeps AI From Authorizing Itself
Design an AI agent authorization architecture that separates proposals, policy, execution, and evidence, including indirect paths that can bypass approval.