Paul Bryant is a Principal Multicloud Architect at Dell Technologies, sharing expert insights on hybrid cloud, HCI, and enterprise IT transformation.
242 articles from this blog
Explains VMware Cloud Foundation lifecycle management as a coordinated operating loop, covering VCF 9.1 capabilities and the limits of zero downtime.
AI agent learning: governing durable changes via provenance, scope, evaluation, approval, and revocation.
AI decision controls: separating model proposals from authorized actions with evidence, permissions, and verification.
Explores how the double-slit experiment offers a mental model for evaluating AI behavior under different conditions, not quantum AI.
AI feedback isn't learning: separating memory, runbooks, and model updates for safe agent governance.
Testing AI agent reliability across the full coordination loop, including failure recovery, evidence preservation, and authority enforcement.
Schrödinger's cat as a metaphor for AI uncertainty: why plausible outputs aren't proven, and how to separate generation, verification, and authorization.
How AI agents should verify before acting: targeted diagnostics, external limits, and separating evidence from execution authority.
VCF 9.1 multitenant design guide using a solar-system model to separate shared platform services from isolated tenant environments.
AI memory architecture: context, RAG, and persistent state — designing reliable use of information across model parameters, retrieval, and workflow state.
AI agent governance: separating evidence, approval, and execution authority to prevent persuasive but unauthorized actions.
AI feedback governance: control which observations become persistent changes, where they apply, and who can authorize them.
Build an offline Python grader for AI agents that separates control, behavior, and outcomes—not just explanations.
Designing stable AI agents: separate retries, reconciliation, and recovery to control interventions, not just model retries.
AI agent verification: prove runtime outcomes, not just tool call success. Separate acceptance, config, convergence, and service results.
How to test AI generalization in incident triage: controlled variations, scoring rubrics, and avoiding shortcut learning and data leakage.
Explore VMware Cloud Foundation 9.1 as a governed platform engine for building and managing private cloud environments.
Explores entropy in AI and human thinking, clarifying why certainty and predictability don't guarantee accuracy.
Designing a shared workspace for AI agents: bounded, versioned task state, controlled interfaces, and atomic updates to coordinate evidence and proposals.
AI reward design: define business outcomes, not easy metrics. Separate permissions, verification, escalation, and efficiency to avoid gaming.
