Agent density is a lifecycle problem, not an isolation problem. Google's own GKE Agent Sandbox benchmark puts the sandbox at 44% and suspend and resume at up to 3.5x—the sandbox is the smaller half. And upstream Kubernetes 1.37 is quietly turning checkpoint and restore into a kubelet primitive. Here's why the economics live in the lifecycle, not the box.
I'm hiring a Senior Developer Relations Engineer for GKE and AI Infrastructure. Instead of describing the work, I built it: a small, playable cluster scheduler that teaches the real problem behind running AI at scale—gang scheduling, memory ceilings, spot preemption, and a TPU slice with actual topology.
The builder's companion to Governed Growth—the same reference repo from the implementer's chair. Why I built a one-tool MCP server to hand an assistant a reliable, governed Google search, how Gemini grounding and citation extraction work in the code, and how a full, human-validated eval picked the default model. The surprise: gemini-3.1-flash-lite matched the larger models on quality, grounded most reliably, and cost the least.
GitHub's REST and GraphQL APIs have separate hourly rate limits—two budgets, not one. Most automation hammers a single bucket while the other sits full. Here's how to see both, why agents drain GraphQL fast, and how routing work across both effectively doubles your headroom.
Traditional SDLC fails for autonomous AI. The Agentic Manifesto introduces the Agentic Delivery Lifecycle (ADLC)—a new operating model for engineering, governing, and tuning non-deterministic systems at scale.
A deep-dive playbook for building scalable, enterprise-grade AI agents. Learn how to move beyond monolithic designs by decoupling tools with a powerful microservices architecture using ADK, the Model Context Protocol (MCP), and Google Cloud Run.
