"Percent of code AI-generated" is this era's lines-of-code: easy to compute from a diff, trivially gamed, and silent on whether anyone can explain the system in six months. Here is what the field data actually shows when authoring gets cheap, why the number you're setting OKRs against measures the stage that stopped being the bottleneck, and four replacement metrics graded by whether you can compute them today.
In one week of late-August 2026, Agent Sandbox, Kueue, and LeaderWorkerSet each shipped a limiter on their own elasticity: a warm pool that refills more slowly, a field that used to be editable and is now frozen, and an autoscaler that ships with a guard against doing what it was asked. The reason they converged now is pod churn against a control plane built for slower turnover and accelerator scarcity that rules out idle capacity—two pressures that turn a refill loop into a thundering herd of its own making. Read at the level of what each controller actually does—the exact flags, the quota invariant, the no-shrink floor—it's the same lesson three times. A control loop you add to smooth a system will, past some load, become the fastest-moving thing in it, and the grown-up move is to bound it. Plus the honest counter-case, because the same week also shipped features that add elasticity.
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.
Explore the evolution from basic LLM interactions to crafting sophisticated System Instructions, and discover how meta-prompting—using AI to refine AI prompts—can unlock more powerful and collaborative AI agents. This post details the journey and a practical approach to building AI that helps you build better AI.
