Building AI agents shouldn’t mean building agent infrastructure. Vertex AI Agent Builder handles the runtime, governance, and Google Search grounding so you can focus on what the agent actually does.
The Agentic Future Is a Governance Problem as Much as a Technology Problem
Most enterprise AI projects don’t fail because the technology doesn’t work. They fail because no one built the infrastructure to let it work at scale.
Most Enterprise AI Is Blind to 80% of Your Data
Your AI reads text just fine. It’s the contracts, recordings, and images it can’t touch that are going to cost you.
You Could Build Your Own Vector Search Stack. You Probably Shouldn’t.
Vertex AI Vector Search 2.0 went GA in March 2026. For ISVs building on Google Cloud, it collapses embedding pipelines, indexing, feature stores, and hybrid search into one managed service, so you can ship AI features instead of building infrastructure.
NotebookLM Enterprise Thinks With You.
Most enterprise AI tools hallucinate on your internal documents because they have never seen them. NotebookLM Enterprise changes the equation by grounding every answer in your actual content, inside your GCP environment.
AI That Understands Your Entire Codebase?
Gemini Code Assist Enterprise gives your engineering team an AI that understands your private codebase, your GCP infrastructure, and your org’s coding standards. For ISVs, it is the difference between faster typing and actually shipping faster.
One Database. Transactions, Analytics, and Vector Search. No Pipelines.
AlloyDB collapses three separate database systems into one managed PostgreSQL instance. The benchmarks are embarrassing for Aurora.
The ETL Pipeline You’re Running Probably Shouldn’t Exist
BigQuery can now run AI models directly inside SQL. The implications for how you’ve been architecting your data stack are a little uncomfortable.