A collection of production-ready Generative AI agent templates built for Google Cloud, designed to close the gap between a promising agent prototype and something you can actually run in production.
The problem it solves
Building a GenAI agent demo is easy; operating one is not. The hard parts show up after the prototype works: how do you deploy and operate it reliably, how do you evaluate whether it’s actually getting better, how do you customize it for a real use case, and how do you observe what it’s doing in production? This repo packages templates that address those four challenges — deployment and operations, evaluation, customization, and observability — as a holistic starting point rather than leaving each team to reinvent them.
The concepts
The templates target Google Cloud’s agent stack, which is squarely my territory as a Customer Engineer there. The recurring pattern: an agent framework for the reasoning loop, Cloud Run or Agent Engine for serving, CI/CD for repeatable deployment, evaluation harnesses to score agent behavior against test cases, and logging and tracing wired in from day one so you can debug an agent’s decisions after the fact — not just its uptime.
How to use it
The repo is notebook-driven (Jupyter), which makes it a good exploration surface: start from a template close to your use case, run through the notebooks to understand the moving parts, then adapt the deployment scaffolding to your own project. I use it as a reference when walking customers through what “production-ready” actually means for agent workloads.
Language: Jupyter Notebook
Source code: github.com/avnit/ai-agent-deploy-ae