Cloud Computing

The flexibility of Google Cloud allows enterprises to build secure and reliable architecture for their AI workloads. In this blog we will look at a reference architecture for private connectivity for retrieval-augmented generation (RAG)-capable generative AI applications. This architecture is for scenarios where communications of the overall system must use private IP addresses and must

In modern cloud architectures, APIs are the fundamental building blocks of applications. However, as organizations scale, these APIs often end up scattered across multiple gateways, teams, and platforms. This sprawl creates blind spots that hinder governance and reusability, but it creates an even bigger problem for the next generation of software: it can block the

Enterprise architects often face a fundamental dilemma: choosing between developer agility and organizational control. Development teams need to move fast and deploy independent microservices without waiting for permission. Security and compliance teams need to be safe, and ensure that data flow is observable and governed by policies. That’s why we built Eventarc Advanced, a serverless

When scaling conversational agents, the data layer design often determines success or failure. To support millions of users, agents need conversational continuity — the ability to maintain responsive chats while preserving the context backend models need. This article covers how to use Google Cloud solutions to solve two data challenges in AI: fast context updates

I live through the same story with every single AI agent. After weeks of experiments and tests, it works like a charm. Suddenly, someone comes with a question that the agent fails to answer properly. I rush to make a change by tweaking one of the prompts. After a handful of tweaks, the failed prompt