Learn to Develop AI Agents on Google Cloud with Vertex AI

Gemini CLI -Authentication with Vertex AI & Service Account Streamlining Gemini CLI Authentication for Enterprise with Vertex AI & Service Accounts! 🚀 For enterprise-grade applications, moving beyond interactive authentication for Gemini CLI is crucial.  Headless authentication via a Service Account with Google Cloud Vertex AI offers enhanced security and  automation. 🔒✨  Here's a quick guide to get your AI tools integrated seamlessly:   1. Configure Environment Variables: Create a .env file in your user directory (C:\\Users\\[username]) with:     * GOOGLE_CLOUD_PROJECT     * GOOGLE_CLOUD_LOCATION     * GOOGLE_APPLICATION_CREDENTIALS (path to your SA key JSON)     * GOOGLE_GENAI_USE_VERTEXAI=true   2. Select Vertex AI: Run gemini and choose option 3 for Vertex AI if not already configured. ⚙️   3. Verify: Use gemini -d to confirm "Authenticated via vertex-ai". ✅ This approach ensures secure and efficient integration of Gemini CLI into your enterprise workflows. 🤖 Read more : https://lnkd.in/ecUYZpFP  #GeminiCLI #VertexAI #GoogleCloud #ServiceAccounts #EnterpriseSecurity #GenerativeAI #Authentication

🤔 Why choosing: Use Vertex AI Agent Engine with BOTH Cloud Run & GKE! One of the most common questions I received is: Can I use Vertex AI Agent Engine services like Memory Bank with my agent running on GKE or Cloud Run? The answer is: YES! You don't have to choose between fully managed memory, or a session and your preferred runtime. You can deploy your agent on Cloud Run and GKE. Instead of managing these agent ops services yourself, you can use Vertex AI Agent Engine. My colleague Vlad Kolesnikov just shared two new, hands-on tutorials that show exactly how to build AI agents with the Agent Development Kit (ADK) and use Vertex AI Agent Engine for its managed Sessions and Memory Bank. Check out the full code in the comments 👇 And stay tuned! We are working on more samples and better documentation to integrate other Agent Engine services with Cloud Run and GKE. #GoogleCloud #VertexAI #AI #GenerativeAI #GKE #CloudRun #Kubernetes #Serverless #LLMs #MLOps #Developers #ADK

🏢 Google Cloud | Leader in Generative AI (IDC MarketScape 2025) Key Highlights / Takeaways: 🔹 Google named a Leader in IDC MarketScape 2025 for GenAI Life-Cycle Foundation Model Software — reaffirming its enterprise AI strategy. 🔹 Gemini 2.5 introduces “thinking models” — with internal reasoning, thinking budgets (cost control), and thought summaries (auditability). 🔹 Vertex AI remains the backbone — offering a full-stack platform across model customization, grounding, and governance. 🔹 The Gemini CLI and improved coding capabilities signal Google’s deeper push into agentic AI tooling for developers. 🔹 Enterprise takeaway: Google is positioning Vertex AI as the go-to environment for production-ready GenAI — from model garden to deployment governance. 💡 Perspective: This recognition cements Google’s pivot from experimental AI to enterprise execution. It’s a clear message — the next AI race isn’t about who trains the biggest model, but who can operationalize it safely, transparently, and cost-effectively. For Azure and AWS, it reinforces the competitive focus: strong model ecosystems aren’t enough without trusted lifecycle management and enterprise-grade controls. #GoogleCloud #VertexAI #GeminiAI #GenAI #ArtificialIntelligence #CloudComputing #AgenticAI #IDCMarketScape #EnterpriseAI #AIPlatforms #Azure

I’m excited to share my latest blog post, "Building Scalable AI Agents: Design Patterns With Agent Engine On Google Cloud," co-authored with Schneider Larbi, Principal Partner Engineer at Google Cloud. We delve into architecture patterns for building enterprise-grade AI agents on Google Cloud, leveraging the Agent Development Kit (ADK), Model Context Protocol (MCP), and Agent-to-Agent (A2A) protocol with Agent Engine Thanks to Ran Li for her special contribution for putting this together to support our valued partners to learn about the future of AI development. Read the full post here: https://lnkd.in/eABaStRr #GoogleCloud #AI #AIAgents #MachineLearning #AgentEngine #Partners #PartnerEngineering #ADK #A2A #MCP #SSP

PwC and @Google Cloud are scaling agentic AI: 250+ enterprise-ready AI agents globally, including 100+ in EMEA—moving organisations from pilots to production with trust by design.   In European healthcare, Limbach Gruppe SE is deploying AI agents across 34 sites on @Google Cloud, aligned with the EU AI Act.   Built on Google Cloud’s agentic stack—Gemini Enterprise, Vertex AI with Gemini models, Agent Development Kit, Agent Engine, Agent2Agent protocol and BigQuery—unified by PwC’s agent OS for governance and integration at scale. Across deployments, clients see up to 8x faster cycle times and over 30% cost reduction in targeted processes, with humans in the loop to ensure judgment and compliance.   Learn more: https://pwc.to/3WCl0iZ   #PwCGoogleCloud #PwC #GoogleCloud #AI #AgenticAI #EMEA #TrustInAI #BusinessTransformation #Ecosystems #Alliances

We're moving into the era of agentic AI—autonomous systems that can execute multi-step tasks securely and at scale. Google Cloud provides the complete toolkit to build them, from the powerful Gemini models and Vertex AI Agent Builder to the critical choice of a vector database for your RAG strategy—be it BigQuery, AlloyDB, or Vertex AI Vector Search. But how do you connect all the pieces? In this carousel, we share our blueprint for building production-grade Agentic RAG on GCP, from the initial data pipelines to secure, scalable delivery. Swipe through to see how to go from prompts to agents. #AIAgents #GoogleCloud #VertexAI #GenAI #RAG 

Data and AI professionals are losing the art of developing true MVPs. Here's how I avoid analysis paralysis and over-engineering to deliver. Let me introduce you to the “3-Resource Rule,” a minimalist and repeatable blueprint for creating AI MVPs. To build a powerful, secure, and low-cost AI demo, you need 3 things: 1. A laptop: A place to run code. 2. A Storage Account: A place to store data. 3. An AI Service: A way to access AI services via API. In my latest Substack article, I break down the 3-Resource Rule architecture that takes you from an idea to impactful demo, quickly. Read the article here: https://lnkd.in/etUhkRRz p.s. I mostly work in Azure but this pattern can be used in other cloud providers or even local development. The concept stays the same.

The AI advantage isn’t custom code—it’s knowing what NOT to build. Your competitors aren’t failing at AI because of technology. They’re failing at strategy. 🎯 I just watched another company spend $500K building a custom AI model that AWS offers for $200/month. Here’s what nobody tells you about AI implementation: The expensive mistake: Building everything from scratch because it feels more “innovative” The smart play: → Use Azure’s pre-trained models for document processing → Leverage GCP’s AutoML for quick prototyping→ Deploy AWS SageMaker when you need scale The companies winning with AI aren’t the ones with the biggest budgets. They’re the ones who know which problems to solve vs. which solutions to buy. Last month, a client cut their data processing time from 3 days to 4 hours. Not with custom code. With the right cloud service configured correctly. Smart AI strategy isn’t about being cutting-edge. It’s about being strategic with what already exists. What’s the biggest AI misconception you’ve encountered? #ArtificialIntelligence #CloudStrategy #BusinessEfficiency #DigitalTransformation #TechLeadership #AWS #Azure #GoogleCloud #Innovation

AI revenue as a key driver for Google Cloud: What an outstanding Q3 earnings call ** Financial Performance and Momentum ** • Revenue Growth: Cloud revenue grew 34% year-over-year to $15.2 billion in the third quarter. Growth in GCP was "much higher than Cloud’s overall revenue growth rate". • Profitability: Cloud operating margin significantly improved from 17.1% in Q3 last year to 23.7% this quarter. • Backlog Surge: The Cloud backlog increased 46% sequentially and 82% year-over-year, reaching $155 billion. This increase was primarily driven by strong demand for Enterprise AI. ** Customer Adoption and Differentiation ** • Major Deal Volume: Google Cloud signed more deals over $1 billion in the first nine months of 2025 than in the previous two years combined. • New Customer Acquisition: The number of new GCP customers increased by nearly 34% year-over-year. • AI Integration: Over 70% of existing Google Cloud customers use their AI products. • Competitive Edge: Google Cloud is the only cloud provider offering its own leading generative AI models (including Gemini, Imagen, Veo, Chirp, and Lyria). Furthermore, nine of the top ten AI labs choose Google Cloud. • Generative AI Revenue: Revenue from products built on Google Cloud's generative AI models grew more than 200% year-over-year in Q3. ** Enterprise Solutions ** • Gemini Enterprise Success: Google launched Gemini Enterprise, the new platform for AI in the workplace, and has already crossed two million subscribers across 700 companies. • Infrastructure Demand: GCP is seeing strong demand for Enterprise AI Infrastructure (including TPUs and GPUs) and Enterprise AI Solutions, driven by models like Gemini 2.5. #Google #GoogleCloud #AI

AI is rapidly transforming industries but outdated data infrastructure is holding businesses back. I'm excited to share this new ebook from the Google Cloud team, which shows how 11 leading organizations are accelerating their data migration to unlock the full potential of their data for an AI-first world. It’s a must-read for anyone looking to drive AI innovation, improve cost efficiency, and empower their teams with faster, more accessible insights. Download now. #Google #GoogleCloud #Data #AI