Gaurav Sareen

People often ask me about the differences in architecture between Amazon Dynamo (the 2007 SOSP paper), DynamoDB (the AWS serverless NoSQL database), and Aurora DSQL (the AWS serverless SQL databases). I captured the key points on durability, high availability, programming model, and trade-offs in my blog: https://lnkd.in/gkMG_tVD TL;DR: Despite the shared name, Dynamo and DynamoDB have little in common architecturally, and make very different trade-off decisions.

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Excited to share that xAI’s Grok models - some of the world’s most advanced LLMs - now run on Oracle Cloud Infrastructure (OCI). Grok will be available through our OCI Generative AI Service, making it easy for businesses to build powerful AI capabilities directly into their applications and workflows, securely and at scale.  xAI now joins OpenAI, Cohere, and other top-tier LLMs on what is rapidly becoming the platform of choice for AI training and deployment. I can’t wait to see what our customers will build! https://lnkd.in/g3PPDt_A

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Today, Microsoft joined Google's developed Agent2Agent (A2A) framework ecosystem to contribute to, and advance the protocol, including interoperability with Azure AI Foundry and Copilot Studio. This is good news for the entire ecosystem! Next to MSFT, we have already welcomed over 50 major Tech players who, together, are shaping the world of AI Agents. Onwards and upwards 🚀

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This interview breaks down the secret sauce behind how Intuit is bringing data, AI, and human intelligence to life for consumers and businesses. As Chairman and CEO Sasan Goodarzi puts it, the real value of AI is unlocked when it’s applied to solve the customer problems that really matter. That’s where we lead. Read the full interview here: https://lnkd.in/gwCArm_q

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re:Invent Day 1 is done and it was packed! My day began with a roundtable with some of our best partners talking about how we can better help customers migrate and modernize their AI workloads and use cases. Then Priya Arora and I presented on-stage on how Agentic AI is opening opportunities for partners, from ISVs and SIs, to startups. We shared data from our survey we did with BCG showing the trends in customer adoption of AI, focusing on the industries and use cases with the highest momentum. We also uncovered areas where customers need the most help and support as they roll out their agentic use cases. After that I sat down with 3 key partners, grabbed lunch with a few AWS peers I don't get to see often enough, and had a lot of ad hoc hallway meetings. (It's hard to walk 10 feet at re:Invent without seeing someone you know) But my highlight was launching the new AI Competency and Agentic AI categories for partners. I had a chance to join the AWS OnAir team to talk about it. My team was the driving force behind the launch and I'm proud of what this means for our partner community. We spent months listening to partners tell us they needed a way to stand out in customer conversations around agentic AI. The new AI Competency creates three distinct specialization paths. Agentic AI Applications recognizes partners delivering production-ready autonomous systems. Agentic AI Tools validates partners providing the infrastructure and tooling that makes agent development possible. Agentic AI Consulting Services distinguishes partners with proven expertise helping enterprises design, build, and scale agentic deployments. Each path requires demonstrated technical depth and validated customer outcomes, not just certifications or marketing claims. We launched with 60 partners who achieved the AI specialization. That's the highest number of launch partners in any AWS Competency program so far. Partners like Loka, Anthropic, LangChain, and Mission are already proving their ability to deploy AI systems that handle real business processes autonomously. That validation matters when enterprises are making critical technology decisions. Partners achieving these specializations gain access to funding, co-marketing resources, and priority placement in customer engagements. But what matters most is the market differentiation. When customers are evaluating dozens of partners claiming agentic AI expertise, this competency provides a clear signal about who has actually done the work. For enterprises evaluating partners, this competency provides the differentiation signal you need. These partners have solved the hard problems of moving agents from demo to production, from single use case to enterprise platform. The timing matters. Enterprises are moving past whether to deploy agentic AI and into how to do it at scale. Partners with proven expertise become increasingly valuable as deployment complexity increases. That's what makes this competency so important.

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NVIDIA just cracked turn-taking in Voice AI. Meet PersonaPlex-7B — an open-source, full-duplex conversational model. Most voice agents are half-duplex (talk → listen → talk). PersonaPlex listens and speaks at the same time, enabling real interruptions and backchanneling (“uh-huh”, “right”). Why it matters • Dual-stream transformer operating directly on audio tokens (no separate ASR/TTS) • Parallel prediction of text + audio for ultra-low latency • Zero-shot persona control: swap personality (text) and voice (audio) at runtime • Built on Moshi, trained on 2,250+ hours of conversational data For real-time support bots and roleplay agents, the ASR → LLM → TTS cascade is obsolete. #VoiceAI #ConversationalAI #GenerativeAI #NVIDIA #OpenSource #AIResearch #LLMs #RealtimeAI

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#AIWorld Highlights: Oracle AI Agent Studio and Marketplace At #AIWorld, I joined Oracle TV to share how Oracle AI Agent Studio and the newly launched AI Agent Marketplace are redefining what enterprise AI can do. Oracle is delivering AI-powered capabilities built directly into Oracle Fusion Applications, helping organizations drive innovation, efficiency, and responsible adoption across every line of business. With #AI_Agent_Studio, customers can design, test, and deploy agents that perform real work—>> automating processes, providing insights, and collaborating with users in context. The new #AI_Agent_Marketplace extends that power, enabling partners and customers to share, certify, and scale industry-specific agent innovations. Here’s what sets Oracle apart: ✔ Support for multiple large language models for maximum flexibility ✔ Built-in observability and collaboration tools for trusted automation ✔ Deep integration with Fusion Applications to deliver measurable outcomes We now have a global network of over 32,000 certified experts and a rapidly growing partner ecosystem accelerating AI adoption worldwide. The future of enterprise AI is built-in, responsible, and ready today. Watch the full interview to see how Oracle AI Agent Studio and Marketplace are empowering businesses everywhere: 🎥 https://lnkd.in/gv6QJ9U9 #OracleAI #AIAgents #FusionApplications #AIWorld #DigitalTransformation

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Amazon Web Services (AWS) today announced a series of innovations that make it easier, faster, and more accessible for adopting and scaling AI and agentic systems. Of the many launches our CEO Matt Garman presented here at re:Invent 2025, I'd highlight three: → #FrontierAgents, a new class of fully autonomous, highly scalable AI agents that can work for hours or days without intervention. #Kiro serves as the virtual developer, AWS Security Agent acts as a security consultant, and AWS DevOps Agent operates as an on-call operations team. → #NovaForge, a solution that empowers companies of any size to build their own AI models by combining proprietary data with Amazon Nova-curated datasets. → #AmazonEC2Trainium3UltraServers, a new generation of processors delivering up to 4.4x more computational performance, 4x greater energy efficiency, and nearly 4x more memory bandwidth. And here's the kicker: we are already working on Trainium4. Today's announcements also include the largest expansion of new models to date in Amazon Bedrock's history, with the arrival of Google’s Gemma 3, MiniMax’s M2, NVIDIA’s Nemotron, OpenAI’s GPT OSS Safeguard, etc. Plus, we launched new Bedrock AgentCore capabilities, Nova 2 models, AWS AI Factories, AWS Security Hub, Database Savings Plans—a flexible pricing model that cuts costs by up to 35%—and more. These aren't just incremental improvements; they represent a phase shift in how we develop technology. It's the beginning of a new innovation cycle that will further accelerate business transformation and democratize access to cutting-edge AI for companies of all sizes around the world and, of course, in Latin America! Check out all the details here: https://lnkd.in/dbtF4EYP #AWS #AWSLATAM #reInvent #reInvent2025 #Cloud #AI #GenAI

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GraphRAG has emerged as a frontrunner in building question-answering systems that enable organizations to extract relevant insights from their interconnected data. In this blog, we demonstrate how to leverage LangChain and Spanner Graph to build powerful GraphRAG applications. Worth a weekend read...!

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Last week, OpenAI committed $300B in cloud compute orders to Oracle. Oracle was up 30%, adding over $200B to its market cap. How will Oracle get this compute? By buying chips from Nvidia. Today, Nvidia has announced that they will invest up to $100B in OpenAI to increase their compute capacity. Stock is up 4% already. So OpenAI is using some of Nvidia's money to pay Oracle, who will then pay Nvidia to buy GPUs, so that Nvidia will then invest some more money into...OpenAI. If it looks like a bubble and acts like a bubble,...

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The rise of Shadow AI - unmanaged external API calls and unvetted models - is creating massive security and compliance blind spots in the enterprise. Are your developers innovating without oversight? The JFrog AI Catalog has evolved to solve this. We now actively detect and inventory every model and API call across your org, instantly bringing them under governance. Learn more: https://bit.ly/4hWlyKz

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Still confused between MCP, A2A, and Functional Calling? These three approaches are reshaping how LLMs and agents operate at scale, but each one solves a very different problem. Let’s break it down 👇 1. Model-Context Protocol (MCP) MCP manages dynamic context windows across enterprise LLM infrastructure. It retrieves prior memory, history, and metadata to personalize every response without hardcoding logic into prompts. 2. Agent-to-Agent Protocol (A2A) In A2A, agents talk to each other directly using shared protocols. Think of it as collaborative problem-solving between specialized local agents, especially useful in multi-agent ecosystems. 3. Functional Calling Here, an LLM acts like a planner. It decomposes a task (e.g., comparing market caps), generates a Directed Acyclic Graph (DAG) of subtasks, and then sends each task to appropriate tools (math, search, APIs) for execution. Conclusion - MCP is about managing context, - A2A is about agent collaboration, - Functional Calling is about task execution. Mastering all three is key to building powerful, production-ready AI systems. Save this visual for your next AI project!

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#Training and #upskilling our customers and partners with the latest data and AI skills is incredibly important to Databricks. That’s why we have opened a new Data + AI Academy in #India, which will support the training of more than 500,000+ data and AI professionals over the next three years. With an increasing need for AI talent, the academy will meet the demands for advanced data and AI skill sets and have a big impact on our customer and partner ecosystem in EMEA. Take a look at the full story here 👉https://lnkd.in/dEGaAgJR Ed Lenta, Rochana Golani, Ankush Korla, Vinod Marur, Anna Lewis

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