Completing the vSphere 8 Exit: Production Acceptance, Recovery, and Day 2 Ownership
Complete the vSphere 8 exit with service acceptance, tested recovery, monitoring, legacy retirement controls, and named owners for Day 2 operations.
observability
VCF 9.1 Operations: Ownership, Observability, and Lifecycle Control
Coordinate observability, automation, lifecycle, capacity, and incident response as one private-cloud service. Define the ownership and readiness criteria that turn monitoring into controlled operational action.
VCF Edge Architecture: Latency, Local Processing, and Recovery
Translate edge latency and resilience requirements into workload placement and operating boundaries. Use a race-operations scenario to examine local processing, communications, security, and recovery.
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.
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.
The Agent Action Evidence Contract: What Every AI Action Must Record
Define a proposed agent action evidence contract that connects identity, approval, execution, independent observation, and recovery while preserving unresolved outcomes.
AI Feedback Loops: Building Systems That Stay Correctable
Keep AI systems correctable as feedback accumulates. Govern knowledge promotion and withdrawal, protect authorization boundaries, reconcile unknown execution outcomes, and test the conditions that should stop the workflow.
Global Workspace Theory in AI: Coordinating Agent Intelligence
Use Global Workspace Theory as a lens for coordinated agents. Design selection, shared state, evidence broadcasting, and authority boundaries without confusing a large context window with a complete coordination architecture.
AI Feedback Loops: Cybernetics and Control Theory for Agents
Design agent feedback around goals, observations, bounded actions, verification, and correction. Use control-theory concepts to examine stability, observability, and the limits of automation.
AI Evidence Contracts: Qualify Data and Calibrate Confidence
Build an evidence contract that keeps observations current, scoped, and traceable. Learn why confidence calibration and evidence qualification answer different questions, and how to use both in an operational workflow.
AI Agent Reliability: Test the Whole Coordination Loop
Test the complete agent coordination loop under failure. Exercise duplicate delivery, interrupted execution, delayed evidence, and changed permissions while measuring task completion separately from control failures.
Schrödinger’s Cat and AI: Plausible Is Not Proven
Separate a plausible AI answer from a verified claim and an authorized action. Use the Schrödinger’s cat analogy carefully, then apply evidence gates to infrastructure decisions.
AI Agents Should Verify Before They Act
Choose diagnostics that can change the next decision. Bound investigation costs, protect diagnostic access, and keep verified evidence separate from approval, execution, and confirmed service recovery.
AI Agent Governance: Evidence Is Not Authority
Keep shared evidence, human approval, and execution authority distinct. Preserve provenance, bind permission to exact operations, and revalidate the boundary when an agent is ready to act.
AI Agent Evaluation: Test the Behavior, Not the Explanation
Evaluate what an agent attempted, what executed, and what happened to the service. Keep control compliance, appropriate behavior, and verified outcomes separate in the scoring and release decision.
AI Agent Verification: Prove the Outcome, Not the Tool Call
Verify the outcome of an agent action beyond the tool response. Separate acceptance, configuration, convergence, and service evidence, then test the conditions that could produce a false success report.
Designing a Shared Workspace for AI Agents
Design a bounded shared workspace for AI agents with versioned state, visible disagreement, and controlled updates. Separate current task state from recovery history and reusable memory.
A Citation Is Not Proof: Building an Evidence and Disclosure Control Layer
Make RAG citations useful evidence rather than decorative references. Validate source identity, claim support, applicability, and disclosure permission while keeping citation destinations under application control.
Infrastructure Change Evidence: What to Capture Before, During, and After a Change Window
Capture the evidence needed to approve, execute, validate, and recover an infrastructure change. Preserve service outcomes, exceptions, decision ownership, and access-controlled records for the next engineer.
AI Context Governance: From Test Results to Production Controls
Turn context-evaluation findings into production controls. Preserve critical evidence through retrieval and summarization, assign ownership, and keep release evidence separate from runtime execution authority.