Brent E.

Did you hear it? … the song of the AI Agents dominating #awsreinvent2025 Nearly every other booth you walk to is demoing an #aiagent platform. In a nutshell, we’re talking about simple ai agents. I’d call them baby agents. A prompt, a RAG, and a few MCPs here and there to collect data, all governed by an agent studio to design agent workflows. Most platforms are promoting an open model framework where you can pick and choose your preferred LLM. Bedrock is one of the key options available. What’s missing? The business application. A lot of those platforms are in their very early stages, many are not even GA yet. What’s common: build from scratch mentality, and my ai agent platform can do everything, everywhere, all at once. IT buyers? Torn down between all of these platforms not knowing if it’s the right time to start choosing one or many. The potential? It’s going to be insanely huge, with businesses and organizations reaching incredible levels of automation, while also providing superior quality and experience to their customers. Get ready as 2026 will be exciting! [sounds surreal to say so, nearly have to apologize that my post did not use any single AI, not even for brainstorming or improving my style. WDYT: Better with or without AI?]

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Starburst has always believed in providing customers with the freedom of choice. Nowhere is this more true than the emerging developments around interoperable compute among data platforms. Recently, I sat down with Snowflake’s Ryan C. Green to talk about the Open Semantic Interchange (OSI), an open, vendor-neutral standard for sharing semantic models across AI, BI, and analytics tools. OSI gives customers a common, open way to define and share business metrics. This helps them stay consistent across dashboards, notebooks, and machine learning models, no matter which tools they use. For Starburst, this furthers one of our core goals. Choice. Customers should be able to keep one trusted set of business definitions inside their Snowflake environment while still having the freedom to choose the tools that work best for them. OSI helps make that possible. It marks an important step in bringing Starburst’s engine to Snowflake-interoperable compute. The video from my chat with Ryan is coming soon 👀 In the meantime, you can get more details from our recent press release: https://lnkd.in/ev2XGB4u #OpenSemanticInterchange #OSI #Interoperability #AI #Data #Snowflake #Starburst

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AMD and Oracle Cloud (OCI) to accelerate drug discovery with Absci, a clinical-stage biotech firm advancing novel therapeutics design with Gen AI. Learn more: https://lnkd.in/g5_Ytjj4 #TogetherWeAdvance_life

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Since James Malone posted something about Snowflake & turned off comments so he doesn't respond to anyone having a different take, I'll comment by reposting it. 1. Storage is cheap when you don't need a ton of it. PB is about $270K a year so I am sure many companies would love to save 30% of that cost by storing it in more performant & compressed format & which they do. Tables in this format happens to be fully managed by Snowflake(easier), have no way to access the data files directly by anyone(more secure) & have 4-5 levels of encryption that get rotated every 30 days and rekeyed in once a year(even more security) They give better performance & security & require no maintenance. Perfect for sensitive datasets or if you prefer not to maintain a lake. 2. if Customers don't want that, with Snowflake they have the option to use lakehouse via Iceberg & Parquet on their own tenant. This is called giving customer an option to pick their own storage method or even mix it up. Malone believes everyone should go opensource(which Delta is not) since it is the ONLY way his product can store & manage data. Per Malone it sounds like Whether customer loses some performance, has more maintanence, pay more for storage and own the responsibility of securing & auditing of all files & access is not important enough as interoperability. Pretty sure not everyone would agree to that. Unfortunately not everyone wants to maintain thousands of files and folders themselves & Be responsible for the file security on top of data access security via the query engines for the sake of interoperability. Sometimes, they just want to load data, query it, use ML & AI on it and not be bothered with upkeep. They want the easy button. Not everyone wants or needs Interoperability but if they do Snowflake offers it and does not force everyone to manage their own storage and that is a really good thing even if Malone thinks otherwise. Comments open for anyone. This is an open discussion.

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The organizations that will execute on AI will be the organizations that have the capability to scale quickly as any AI innovation happens. Open AI just announced GPT 5.2, and not only is Databricks the only platform that already supports it, but we used our benchmark to measure it against enterprise tasks.

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Strong example of what responsible AI done right actually looks like. This piece by Lisa Schneider highlights why the Team Select and Cuesta Partners Silver Anthem Award matters beyond the trophy. This is not AI theater. It is applied, operational, and designed for real-world adoption in healthcare. A few things that stood out to me: • Data first, always. The AI model succeeded because the data foundations were built correctly first. Dashboards, business data models, and shared definitions delivered value immediately and reduced downstream risk. • Problem-led AI, not tech-led AI. The team resisted the urge to boil the ocean. They focused on a narrow, high-impact use case where early signals could genuinely change patient outcomes. • Interpretability over black boxes. Risk scores alone are useless in clinical environments. Surfacing contributing factors made the model trustworthy and actionable for care teams. • Workflow is the product. Adoption was designed, not assumed. Clear operating models, clinical co-design, and on-site alignment made the difference between a model that exists and one that gets used. This is the kind of AI story Tech leaders should be studying. Not because it is flashy, but because it is repeatable. Build the data muscle. Pick the right problem. Design for humans. Then scale. Awards are nice. Fewer hospitalizations and better patient outcomes are better. Well worth the read to see how AI that actually improves lives!

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Why LLMs Require a Dedicated Evaluation Framework Unlike traditional software, LLMs produce probabilistic outputs. Without systematic evaluation, enterprises risk mistaking demo success for real-world readiness, leading to failures when models are deployed at scale. Popular Frameworks & Tools : RAGAS, DeepEval, LangSmith, UpTrain and llamaIndex. https://lnkd.in/gN3MNXU7

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AI agents just leveled up. 🤖 And they’re working inside your data cloud.⚡ Snowflake just dropped MCP Servers, giving AI agents tools, memory, and secure access to enterprise data. 📍 Why it matters: Large enterprises can now operationalize agentic AI directly within Snowflake, streamlining everything from fraud detection to customer onboarding, without duplicating infrastructure or compromising security. Here’s what it unlocks: • Shared long-term memory and secure retrieval from enterprise data • Agent-native access to tools like SQL, ML models, and third-party APIs • Full lifecycle management, all within your Snowflake environment 💡The implications are massive: reduced data fragmentation, faster AI time-to-value, and a secure foundation for enterprise-wide agent orchestration. Is your data ecosystem ready for this level of AI maturity? What would you automate with agentic workflows? #EnterpriseAI #DataStrategy #FutureOfWork #CLevelStrategy #AI #Snowflake https://lnkd.in/e72sVtUP

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