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Hi, I'm Casey West 👋

Casey West

As a Lead Developer Advocate at Google Cloud, I'm focused on defining the playbook for the era of agentic AI. Here, I share what it takes to build and lead—from the code and architectures that power autonomous systems to the engineering cultures required to make them thrive.

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Governed Growth, Part 2: The Retention Window That Quietly Shrank

Governed Growth, Part 2: The Retention Window That Quietly Shrank

On February 23, 2026, Google quietly made the retention terms for Grounding with Google Search better: the window dropped from thirty days to up to three, and what's kept narrowed from your prompts and output to short-lived debug logs. The old thirty-day, prompts-and-output language didn't vanish—it now describes Grounding with Google Maps, a sibling capability under the same terms. Here's what the corrected terms actually say, how to verify the date yourself from Google's dated archive, and the durable lesson: govern facts that move—cite the dated primary source, hedge the number, and don't hard-code a digit that will drift out from under you.

Building gemini-search-mcp: Grounding, Citations, and Choosing a Gemini Model

Building gemini-search-mcp: Grounding, Citations, and Choosing a Gemini Model

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.

Governed Growth, Part 1: Better Together, Governed Apart

Governed Growth, Part 1: Better Together, Governed Apart

Opening a platform to more teams and more models—first-party and partner, such as Gemini and Claude—quietly assumes those capabilities share one trust boundary. They don't. Multi-model expansion is a governance-maturity story: Google Cloud lets you grant capability broadly while keeping each model's data path a deliberate, fine-grained decision. You can grow usage without growing exposure.