Luca Roviaro

Our commitment at Google Cloud is to bring our leading AI capabilities to customers where they are. I'm thrilled to announce our expanded partnership with Oracle, which will make our #Gemini models available to customers via #VertexAI and the Oracle Cloud Infrastructure Generative AI service. Oracle is also collaborating with Google Cloud to make Gemini models available within Oracle Fusion Cloud Applications. This collaboration is a significant win for enterprises, enabling them to bring Google Cloud's powerful AI directly into their core data platforms and business workflows. https://lnkd.in/gPdtPXCH

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🌐 𝐓𝐡𝐞 𝐈𝐝𝐞𝐚 𝐨𝐟 𝐚𝐧 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐋𝐚𝐲𝐞𝐫 In the new AI-driven enterprise architecture, organizations are moving from rigid, process-centric systems toward agentic systems—where autonomous or semi-autonomous agents handle tasks, orchestrate workflows, and continuously learn and adapt. An 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐋𝐚𝐲𝐞𝐫 is the middle man (the connective tissue) that: ✅ Mediates between agents and systems (A2A + MCP). ✅ Provides governance, observability, and control (through policies) ✅ Transforms and enriches data flows so agents can make better decisions in real time. 🚀 𝐖𝐡𝐲 𝐌𝐮𝐥𝐞𝐒𝐨𝐟𝐭 𝐢𝐬 𝐚 𝐩𝐞𝐫𝐟𝐞𝐜𝐭 𝐟𝐢𝐭 MuleSoft is uniquely positioned to serve as this Agentic Transformation Layer because of its core strengths: ⭐ 𝐂𝐨𝐦𝐩𝐨𝐬𝐚𝐛𝐢𝐥𝐢𝐭𝐲 MuleSoft already treats every capability as an API, making systems composable and consumable by agents. Now agents and tooling become the new building blocks. Agents can discover, invoke, and orchestrate other agents and tools seamlessly. ⭐ 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 + 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 Agents rarely work in isolation; they need to be specialised with a certain domain (CRMs, ERPs, HR, etc.). MuleSoft Agent Fabric (Broker) establishes agent interaction by registering specialised agents for a domain or subdomain. ⭐ 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 & 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 With API Manager and Anypoint platform, MuleSoft enforces policies, authentication, throttling, and monitoring. This prevents agents from making uncontrolled or insecure calls into critical systems. ⭐ 𝐃𝐚𝐭𝐚 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 & 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐄𝐧𝐫𝐢𝐜𝐡𝐦𝐞𝐧𝐭 Agents need clean, contextual data to reason effectively. MuleSoft’s DataWeave can transform raw system data into agent-friendly formats (e.g., JSON, vector embeddings, domain-specific schemas) leveraging MCP capabilities. ⭐ 𝐄𝐯𝐞𝐧𝐭-𝐃𝐫𝐢𝐯𝐞𝐧 & 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 Agents must react to events (e.g., new lead in Salesforce, payment failure, IoT alert). MuleSoft supports streaming and event-based patterns, enabling agents to act in real time. ⭐ 𝐇𝐲𝐛𝐫𝐢𝐝 + 𝐌𝐮𝐥𝐭𝐢-𝐂𝐥𝐨𝐮𝐝 𝐑𝐞𝐚𝐜𝐡 MuleSoft connects on-prem legacy with modern SaaS and cloud-native platforms. This allows AI agents to operate across the entire enterprise landscape, not just new systems. 📌 Learn more at: https://lnkd.in/gXPCFd3m #mulesoft #salesforce #agent #a2a #mcp #mac

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Amazon Bedrock now supports Global Cross-Region inference for Anthropic Claude Sonnet 4. You can now use the Global Claude Sonnet 4 inference profile to route your inference requests to any supported commercial AWS Region for processing, optimizing available resources and enabling higher model throughput. https://lnkd.in/gyirkyH7

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Today, we’re excited to announce the release of the A2A .NET SDK – an implementation of the Agent2Agent (A2A) protocol that enables .NET developers to build both A2A servers and clients. With this SDK, you can now create agents that seamlessly communicate and collaborate with other agents in the ecosystem, whether they’re built with .NET or any other technology that supports the A2A protocol. This release strengthens the broader exisitng AI ecosystem, particularly for developers working with Azure AI Foundry and Semantic Kernel. Semantic Kernel already includes A2A agent capabilities through its Python and .NET implementations, and this community-driven .NET SDK enables the Semantic Kernel team to adopt the latest A2A protocol features more rapidly while maintaining the framework’s enterprise-grade stability. https://lnkd.in/eKp2wT9X

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Global spending on cloud infrastructure services reached $95.3 billion in Q2 2025, up 22% year on year. According to Canalys (part of Omdia), cloud demand increased due to AI consumption, revived legacy migrations, and cloud-native scale-ups. As hyperscalers advance their AI capabilities and applications, more customers are adopting multi-model approaches to meet specific cost and use-case requirements. In Q2 2025, Amazon Web Services (AWS), Microsoft Azure and Google Cloud continue to dominate this market with a 65% combined market share of global cloud infrastructure spending. Collectively, customer spending with these three hyperscalers increased 27% year on year. Microsoft Azure (39% y/y growth) and Google Cloud (34% y/y growth) continue to outgrow market leader AWS (17% y/y growth). When taking AWS share lead into account (bottom chart), in actual dollar terms, AWS’s year-on-year increase outpaced that of both Microsoft and Google Cloud. Hyperscalers are experiencing a significant increase in customer demand, with growth driven by AI-related workloads alongside a rebound in traditional migrations and continued capacity expansion by cloud-native enterprises. Investment in AI infrastructure continues to accelerate. In July, Google lifted its 2025 capital expenditure target from US$75 billion to US$85 billion; earlier, AWS projected total spending for 2025 to exceed US$100 billion, while Microsoft announced plans to invest approximately US$80 billion in infrastructure expansion in the current fiscal year. Yi Zhang of Canalys (part of Omdia) pointed out a key trend that customer demand for AI services is evolving from a primary focus on availability and ease of use to a greater emphasis on flexibility and fit-for-purpose model choice. An increasing number of enterprises are seeking the capability to switch between different AI models based on specific business requirements, enabling them to achieve an optimal balance of performance, cost and application fit. Amid this trend, AWS Bedrock, Azure AI Foundry and Google Vertex AI continue to broaden their portfolios of proprietary and third-party models, spanning the full spectrum of capabilities from high-complexity reasoning to low-latency response, thereby supporting a wider range of industries and workloads. Much like the entire $5.3 trillion tech industry, coopetition has become the norm in the generative AI landscape: vendors compete on model advancement and product capabilities even as they collaborate on compute capacity and model distribution. For example, AWS Bedrock aggregates models such as Anthropic’s Claude and OpenAI’s GPT, while OpenAI has added Google Cloud to its compute network to bolster capacity.

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This is a significant development in the cloud and AI space. Oracle will now offer Google's Gemini AI models, enabling businesses to leverage both platforms seamlessly. A great move that highlights the growing demand for integrated, multi-cloud solutions. #AI #CloudComputing #Partnership #GoogleCloud

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