Preparing for Agent-Native Infrastructure with Google Cloud

Heading to Google Cloud Next '26 this week. The conversation has clearly moved beyond GenAI. What’s emerging now is something more structural: systems that don’t just generate, but act. But here’s what I am more interested in: Not the agents themselves. The systems they depend on. Because once you move from “assistive AI” to “autonomous execution”: 🔹 Data isn’t just consumed, it drives decisions in real time 🔹 Pipelines aren’t just transformations, they become part of execution flows 🔹 Governance isn’t about access anymore, it’s about controlling actions And this is where things get harder. Most platforms today weren’t designed for this level of coordination: - fragmented data foundations - inconsistent execution patterns - governance models built for users and not autonomous systems That changes the problem entirely. It’s no longer: “Do we have the right models?” It’s: “Do we have a platform that can support autonomous systems, without introducing new failure modes at enterprise scale?” Curious how others are thinking about this shift. #GoogleCloudNext #DataPlatform #AIPlatform #PlatformEngineering #AgenticAI

Google just made a quiet but powerful move… #Gemma4 is now on Google Cloud. At first glance, it looks like another model release. It’s not. This is a clear step toward open, enterprise-grade AI at scale. What stands out 👇 👉 More capable, more efficient models 👉 Multimodal by design (text, images, audio) 👉 Large context windows for real-world use cases 👉 Built to run both in the cloud and on your own infrastructure But the real shift is this: We’re moving beyond chat. This is about AI that can execute. Gemma 4 is designed for: • Multi-step reasoning • Workflow automation • Code generation • Tool usage and structured outputs In simple terms… From “AI that responds” → to AI that gets things done. And the most important part: Businesses can now deploy these systems → On their own data → Within their own environments → With full control over security and compliance That changes adoption completely. Because the real challenge was never access to AI… It was trust and control. The takeaway: The future won’t be built on prompts. It will be built on AI agents running inside businesses. And this is exactly where things are heading. #AI #GoogleCloud #AgenticAI #Automation #LLMs #Innovation

Cloud-native changed how we build applications. But what comes next? Engineering teams are now moving toward AI-first development—where AI is not just an add-on, but embedded across platforms, workflows, and products. Within the Google Cloud ecosystem, this shift is accelerating fast: → Infrastructure, data, and AI are converging → Developer workflows are becoming AI-assisted → Platforms are evolving into full development ecosystems On April 8, we’re bringing together industry leaders to discuss what this transformation actually looks like in practice—and what it means for engineering teams today. Join us for Cloud & Development Trends in 2026: From Cloud-Native to AI-First Engineering Register at: https://lnkd.in/dzRC_kB7 #SoftwareEngineering #GoogleCloud #TechLeadership

#AI-First Engineering is becoming the number 1 initial topic of discussion amongst Tech Leaders. It's no longer a matter of "if" or "when". It's happening now and BairesDev is right there in front.

Cloud-native solved a real problem. It gave engineering teams speed, portability, and scale they didn't have before. AI-first is a different kind of shift. The question isn't whether to use AI. It's whether you're building it into how your teams actually work or just adding another tool to the stack. Inside Google Cloud right now, infrastructure, data, and AI are converging. Developer workflows are getting AI-assisted. Platforms are becoming full development ecosystems. That's not a product announcement. That's a structural change in how software gets built. The teams winning in this environment are the ones treating AI as a workflow question, not a feature question. On April 8, a group of industry leaders are getting into what this actually looks like in practice. Worth your time if you're thinking seriously about what engineering leadership looks like in 2026. Details below.

Great article from Google on why current cloud infrastructure wasn't designed for enterprise-grade Agentic AI deployment. It's also exactly what MeshAgent was built to solve. (https://www.meshagent.com) Agent systems don't stall in production because of model quality. They stall on governance, observability, cost, complexity, and integration. So we solved those concerns as part of the MeshAgent platform: Secure, on-demand Rooms where humans, agents, tools, and services collaborate over shared context , with file storage, queues, databases, and email support built in Sandboxed code execution so agents can write and run code safely without escaping their boundaries Granular participant tokens, API scopes, secrets, and OAuth flows for fine-grained access control Built-in OpenTelemetry logs, traces, metrics, usage dashboards, and per-agent cost attribution An in-room LLM proxy for visibility, control, and automatic API key isolation Everything you need to build, deploy, and operate agents, quickly, safely, and at scale. #AgentNative #AI #AgentOps #MeshAgent #EnterpriseAI

𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 𝗡𝗲𝘅𝘁 𝗶𝘀 𝗯𝗿𝗶𝗻𝗴𝗶𝗻𝗴 𝘁𝗼𝗴𝗲𝘁𝗵𝗲𝗿 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝗮𝘁 𝗮 𝘁𝘂𝗿𝗻𝗶𝗻𝗴 𝗽𝗼𝗶𝗻𝘁 𝗳𝗼𝗿 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜. AI is moving from experimentation to becoming a production layer of the enterprise. Many organizations struggle to make this happen quickly enough. 𝗔𝘀 𝗮 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 𝗣𝗿𝗲𝗺𝗶𝗲𝗿 𝗣𝗮𝗿𝘁𝗻𝗲𝗿, 𝘄𝗲 𝗯𝗿𝗶𝗻𝗴 𝘁𝗵𝗲 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝘁𝗼 𝗺𝗮𝗸𝗲 𝗶𝘁 𝗿𝗲𝗮𝗹: • Embedding AI into software delivery • Scaling agentic workflows with governance • Modernizing platforms to support AI at scale Our team will be on the ground in Vegas connecting with leaders working through these challenges. 📍 Google Cloud Next 📅 April 22–24 📌 Las Vegas, NV 𝗜𝗳 𝘆𝗼𝘂 𝗮𝗿𝗲 𝗮𝘁𝘁𝗲𝗻𝗱𝗶𝗻𝗴, 𝘀𝗲𝗰𝘂𝗿𝗲 𝘀𝗼𝗺𝗲 𝘁𝗶𝗺𝗲 𝘄𝗶𝘁𝗵 𝗼𝘂𝗿 𝘁𝗲𝗮𝗺 𝗼𝗻𝘀𝗶𝘁𝗲: https://okt.to/mn4tZ2 #GoogleCloudNext #AIinProduction #CloudAI #EnterpriseAI #Xebia

The "AI in Production" concept is more than just the models—it's about data and security foundations to make Agentic AI systems work at scale. I'll be heading to #GoogleCloudNext to dive into these updates, specifically looking at how we can build robust, secure data systems for agentic workflows. Who else is going to be in Vegas? I’d love to meet up, share notes on the latest in Agentic AI data, security, and infrastructure, and talk shop. Let me know if you'd like to meet up.

Enterprise AI is reaching a pivotal moment—transitioning from trial phases to significant production results. The focus has shifted from whether to implement AI to how swiftly it can be scaled efficiently. Excited for the discussions at Google Cloud Next. 👇 #GoogleCloudNext #EnterpriseAI #AIInProduction #CloudAI #GenerativeAI #AIAtScale #DigitalTransformation #Xebia

Google Cloud just published a case study on Trillet AI. We provide the voice application layer for enterprises automating high-stakes customer interactions. Healthcare scheduling, legal aid, government follow-ups. Conversations where precision and empathy both matter. Our platform on Google Cloud resolves 85% of complex calls without human intervention. A few numbers from the case study: ↳ Sub-1% error rate with Gemini in Vertex AI ↳ Sub-2-second response latency via GKE ↳ 80% reduction in infrastructure costs ↳ 10% conversion lift for legal services clients The stat we're most proud of: in legal aid deployments, callers reported feeling heard during stressful situations. That's the bar we build to. A Melbourne-built platform, now featured on Google Cloud's customer page. Read the full case study: https://lnkd.in/gZ7RQUUJ