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Gemini Enterprise and Agentspace, Enterprise Agents for Every Employee (Google Cloud Gen AI Series, Part 22)
Google Agentspace is now Gemini Enterprise, the per seat front door that puts search and a gallery of agents in front of every employee. What it includes, how a request flows, what a seat costs, and when to buy it instead of building your own.
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Multi-Agent Systems on Vertex AI with ADK and Agent Engine (Google Cloud Gen AI Series, Part 21)
One big agent with twenty tools rots fast. Here is how to split it into a coordinator and typed sub-agents with the ADK, choose deterministic versus LLM-driven flows, connect across boundaries with A2A, and deploy to Vertex AI Agent Engine.
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TPU Pods and Multislice Distributed Training on GKE (Google Cloud Gen AI Series, Part 19)
Where a single TPU slice stops fitting your model, Multislice takes over. How v6e Pods, ICI and the DCN, and GKE JobSets scale training from 16 chips to thousands.
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Gemma Open Models on Vertex AI, from Model Garden to Endpoint (Google Cloud Gen AI Series, Part 18)
When a managed Gemini bill stops making sense, Gemma is the open model you host yourself on Vertex AI. Here is the 2026 lineup, the deploy path through Model Garden, and the volume where self-hosting actually pays.
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Distilling Gemini on Vertex AI, from Teacher to Student Model (Google Cloud Gen AI Series, Part 17)
Distillation trains a small Gemini student to copy a large teacher, so the teacher writes the labels and you serve the result at Flash prices. When it pays, what it costs, and where it fails.
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Fine-Tuning Gemini with Supervised Tuning on Vertex AI (Google Cloud Gen AI Series, Part 16)
Supervised fine-tuning on Vertex AI adjusts Gemini to your task with a few hundred labelled examples, and because it uses LoRA the tuned model costs the same to run as the base. Here is when to tune, how to build the dataset, the knobs that matter, and what it costs.
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Grounding Gemini with Google Search and Your Own Data (Google Cloud Gen AI Series, Part 15)
How to ground Gemini on Vertex AI against the live web and your own Vertex AI Search data store, read the grounding metadata, render citations correctly, and size what it costs.
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Vertex AI Safety Filters and Model Armor (Google Cloud Gen AI Series, Part 14)
Gemini safety filters and Model Armor are two separate layers on Vertex AI. Here is what each one catches, why the built-in filters default to off, and the configuration I would actually run in production.
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Vertex AI Agent Builder and the ADK, From Local Agent to Managed Runtime (Google Cloud Gen AI Series, Part 13)
Build an agent in code with the ADK, deploy it to the managed runtime, and see what the always-on replica really costs before your first user shows up.
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Vertex AI Search and RAG Engine, from Data Store to Grounded Answer (Google Cloud Gen AI Series, Part 12)
Vertex AI Search gives you managed retrieval with almost no plumbing, while RAG Engine hands you the chunking, embeddings, and vector backend. Here is when each one wins.
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Calling Gemini Models with the API, Streaming, and Function Calling (Google Cloud Gen AI Series, Part 11)
The legacy Vertex AI generative SDK was removed in June 2026, so every Gemini call now goes through one library. Here is how to call a model, stream the answer, and wire function calling without letting it run away with your latency.
Architect’s Toolkit
PJ’s Tools
VMware Cloud Foundation
- VCF Documentation
- VCF 9 Planning & Preparation Workbook
- VCF Bill of Materials (BoM)
- VMware Compatibility Guide
- VMware Interoperability Matrix
- VMware Configuration Maximums
- VMware Ports & Protocols
- VMware Hands-on Labs
- RVTools Download
Nutanix
AI & Cloud-Native Platform
- NVIDIA Build (Model Catalog)
- NVIDIA AI Enterprise Reference Architecture
- NVIDIA NIM Performance Benchmarking
- NVIDIA NGC Catalog
- NeMo Microservices Helm Chart
- Helm Charts Repository
- Hugging Face Models
Architecture & Design
About the Author
Dr Pranay Jha
Dr. Pranay Jha is a Cloud and AI Consultant with 18+ years of experience in hybrid cloud, virtualization, and enterprise infrastructure transformation. He specializes in VMware technologies, multi-cloud strategy, and Generative AI solutions. He holds a PhD in Computer Applications with research focused on Cloud and AI, has published multiple research papers, and has been a VMware vExpert since 2016 and a VMUG Community Leader.
Recommendations & Endorsements
Pranay has very deep technical knowledge and exceptional capabilities. He has strong ability to well-manage complex situations and deliver the customer expectations with high standards of satisfaction. We worked together in one of most complex migration projects and Pranay delivered flawlessly.
Pranay was my VMware Architect on one of our financial accounts at IBM. He was instrumental in documenting and recommending our reference architecture. As a Chief Architect, you are only as good as your team that supports you. Pranay was top notch.
Pranay is proactive, result oriented, responsible and technically sound. Always ready to put all his energy to get the job done. Exceptional troubleshooting skills in Windows Server, virtualization and VMware — a great asset to any company.
Pranay is one of the most talented and dedicated persons I have ever worked with. His vast knowledge from different domains enables him to discuss and help on almost every topic in IT. Needless to say, he is expert of experts in VMware.
DrJha