How to Run Coding Agents Safely Inside CI/CD Pipelines
Guide on securely integrating coding agents into CI/CD pipelines with sandboxing, read-only access, and human approval stages.
Paul Bryant is a Principal Multicloud Architect at Dell Technologies, sharing expert insights on hybrid cloud, HCI, and enterprise IT transformation.
176 articles from this blog
Guide on securely integrating coding agents into CI/CD pipelines with sandboxing, read-only access, and human approval stages.
A tutorial on deploying NVIDIA NIM microservices on Kubernetes using the NIM Operator for GPU-backed inference.
A model for matching AI agent autonomy to access, risk, and reversibility in enterprise workflows, addressing the blast-radius problem beyond RBAC.
Guide to deploying NVIDIA NIM in air-gapped environments, covering dependencies, artifact planes, and validation for disconnected enterprise AI.
Explains how to give AI agents secure identities using workload identities, delegated authority, and token exchange instead of sharing human credentials.
Explains why prompt libraries are insufficient for agentic AI and how to translate prompt intent into enforceable execution policies.
Comparison of VMware Cloud Foundation 9.0 and 9.1, detailing key changes, management architecture, and operational improvements for private cloud.
Designing a trusted agent controller for enterprise AI, focusing on deterministic state machines and security boundaries.
A guide on safely deploying AI model, prompt, and tool changes using canary releases and automated rollbacks.
Guide to sharing NVIDIA GPUs in Kubernetes using MIG, time-slicing, and ResourceQuotas for improved utilization.
Explores the critical security boundaries for AI agents in enterprise environments, defining what actions agents must never be allowed to perform autonomously.
Guide to installing and configuring the NVIDIA GPU Operator on Kubernetes for GPU workload management.
Explains why MCP should be a tool plane, not the complete agent controller, in enterprise AI systems.
Compares VMware VVF 9.0 and VCF 9.1, explaining the difference between a workload platform and a private cloud platform.
Analysis of protocol-layer security for AI agents using MCP and A2A, focusing on gateway-level control and enterprise security architecture.
Checklist for moving AI agents from prototype to production, covering identity, state, tools, policy, observability, and governance.
Guide for planning and executing a VCF 9.0 to 9.0.1 maintenance upgrade, emphasizing preparation and risk avoidance.
A reference architecture for private AI on VMware Cloud Foundation 9.1, covering sovereignty, identity, GPU architecture, and operations.
Explores when to keep AI on-premises based on data gravity, latency, sovereignty, and cost for enterprise workloads.
Explains the importance of defining orchestration boundaries in enterprise LLM applications, focusing on architecture decisions over framework choices.