What if your data teams could get answers to complex questions in minutes instead of hours? That’s what Carrefour set out to solve with its Data Platform Assistant, an AI agent built on Google Cloud and integrated directly into Google Chat. Using Google Cloud ADK, Gemini, Agent Search, Cloud Run and BigQuery, the assistant taps into internal knowledge to provide accurate answers and sample SQL queries — right where employees already work. A great example of how agentic AI can reduce repetitive work and free up engineering teams for higher-value challenges. Read the full story → https://goo.gle/4dV8DYq
Google Cloud
Software Development
Mountain View, California 3,461,841 followers
The new way to cloud.
About us
Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.
- Website
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https://cloud.google.com/
External link for Google Cloud
- Industry
- Software Development
- Company size
- 10,001+ employees
- Headquarters
- Mountain View, California
Updates
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Best Buy is transforming its retail experience with the power of AI. By deepening its collaboration with Google Cloud — leveraging Gemini Enterprise for Customer Experience, BigQuery, and Google Cloud Consulting — Best Buy has built an AI-powered foundation to support its customer experience goals. The impact is already clear: self-service call containment has increased by over 50%, transfer rates have dropped by 1.5% to 2% with faster resolution times, and CX development cycles have shrunk from months to mere weeks. 🎬 See how smart tech foundations are actively shaping the future of retail care → https://goo.gle/4hJVBOq Learn more → https://goo.gle/4hL4HdZ
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On this week's livestream, Arthur Soroken (Co-founder of Google AI Futures Fund) sits down with Amir Sadeghian, PhD and Ali Sadeghian, the brother-duo and co-founders behind Astrocade. They'll discuss how generative AI and Gemini multimodal models are redefining game development by enabling creators to build, customize, and publish personalized interactive content through natural language prompting. And they'll even break some news, discussing a previously unannounced initiative!
Meet the founders of Astrocade: Build your own video game with just text
www.linkedin.com
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Introducing Google Cloud Modernize: an end-to-end portfolio designed to collapse multi-year transformation roadmaps with agentic AI. 𝗪𝗵𝘆? Every enterprise wants to build autonomous AI agents, but agents are only as capable as the systems they can access. When core business logic and operational data are trapped inside legacy applications and fragile dependencies, the architecture becomes the ultimate bottleneck to AI innovation. You can’t build next-generation agentic innovations on top of locked-away legacy systems. 𝗪𝗵𝗮𝘁’𝘀 𝗶𝗻𝘀𝗶𝗱𝗲 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 𝗠𝗼𝗱𝗲𝗿𝗻𝗶𝘇𝗲? • Modernization Hub: Assess and accelerate the transformation of mainframe, .NET and Java applications • Container Migration Agent: Automate cross-cloud container transitions • Agentic Assessments: Run defensible cost models and assessments in minutes with Migration Center. Don’t let technical debt dictate your transformation timeline. Learn more → https://goo.gle/3VulDOu
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Let's get your agents into production! Take this course in the Gemini Enterprise Agent Ready (GEAR) learning path to learn to design hierarchical agent trees and deterministic workflow agents while identifying the ideal hosting environment to ensure your AI agents are secure, scalable, and production-ready → https://goo.gle/3TE48dO
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In a race against time, McLaren Racing prepared for Sepang with the help of Gemini. Watch more → https://goo.gle/3TCMdUP #BahrainGP
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Is your team leaving performance on the table in latency-critical workloads? Traditional profiling relies heavily on senior engineers spending weeks manually tuning C++, Swift, or CUDA code. With AlphaEvolve, Google Cloud introduces an autonomous evolutionary loop that pairs Gemini’s architectural reasoning in the cloud with domain-specific benchmark harnesses on your target infrastructure. Key takeaways from our recent video processing implementation with DoIt: -Zero compromise on quality: Automated structural fidelity gates keep latency optimizations from degrading output. - Systemic discoveries: The system finds multi-frame lifecycle optimizations rather than trivial inlining. - Universal applicability: While demonstrated on video pipelines, the split-loop pattern scales across database latency, microservices throughput, and tensor calculations. See how autonomous performance optimization is shifting the developer paradigm → https://goo.gle/4xWUkts
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Google was named a Leader in the 2026 Gartner Magic Quadrant™ for Container Management and positioned highest for Ability to Execute. According to the report, "The container management landscape is maturing rapidly as enterprises prioritize AI and complex application architectures. Infrastructure and Operations leaders must prioritize vendors delivering AI capabilities, including GPU orchestration, agent infrastructure, operational consistency and robust security, to drive modernization at scale." Download the complimentary report to learn more → https://goo.gle/4z9s347
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What does it mean to be a 10x engineer in the age of AI? Hint: it’s not 10x output. Nir Szilágyi, Sr. Director of Engineering at PayPal, explains that the modern 10x engineer is a "systems thinker" who empowers their team and solves the hard problems, with AI as an amplifier. Watch Nir’s full interview with Oliver Parker by registering for our ROI of AI video series → https://goo.gle/4yoPw1d
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If your testing strategy for AI agents consists of running 3 manual prompts in your terminal and saying “looks good to me,” you don’t have an agent ready for production—you have a prototype. The hardest part about autonomous loops (like LangGraph or CrewAI) isn’t hard crashes—it’s silent failure. Agents will execute without throwing a single 500 error while quietly hallucinating or generating subpar outputs. In episode 3 of the AI Agent Clinic, Dani Zamora sits down with Matthew Feroz, Developer Advocate at Merge, to upgrade his DocsHound agent in 60 minutes. At first, you'll see that Matt was confident in his agent’s output. But once we hooked up an automated evaluation pipeline, the data told a different story: a 33% quality score on documentation accuracy—a complete blind spot that manual testing never caught. In this episode, we break down the 4-step framework to evaluate any AI agent: 1️⃣ Map Execution Flow: Pointing coding agents to source code to inspect inner workings. 2️⃣ Standardize Telemetry: Using OpenTelemetry and OpenInference so your eval toolset works across any framework. 3️⃣ Define Quality Rubrics: Turning subjective developer expectations into structured LLM-as-a-judge metrics. 4️⃣ Visualize Direction: Running scorecards to spot exact regressions in latency, token cost, and accuracy. Check out the full 60-minute build → https://goo.gle/4hHEXiO
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