Ashvin Kannan

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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Exciting update - AI Mode just launched in India, as an experiment on Search Labs. It’s a whole new way to Search, one that’s conversational, contextual and built for how we naturally think and ask. Powered by a custom version of Gemini 2.5, AI Mode brings Google’s most advanced AI capabilities into Search, helping you tackle those complex questions that don’t have simple answers. Whether it’s planning a multi-city holiday, comparing specs across products or digging into a complex how-to, AI Mode allows you to ask it all in one go and get nuanced AI-generated responses, complete with helpful links and the ability to ask follow up questions. Try AI Mode for yourself → https://lnkd.in/ghfNMtFC and tell us what you think!

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Throughout history, the pattern repeats: new tools emerge and human capability gets amplified. We're entering a new era where interdisciplinary collaboration drives discovery at a rapid pace—an era where AI is in the human loop, not the other way around. This is creating massive opportunities to solve problems that truly matter. And in the coming year, I see this shift impacting everything. From how we combat loneliness and administer care to those that need it most, to how developers design and build systems, and most importantly, how we educate ourselves in a time of rapid change. Read my full tech predictions for 2026 and beyond https://lnkd.in/eHjPnxYB

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We all assumed GenAI’s breakout use case would be productivity. Turns out, it might actually be… therapy. A recent HBR piece shows that people are turning to AI not to write memos or crunch data — but to reflect, vent, and seek emotional support. Sure, it’s based on a limited sample — but it resonates. Funny enough, the very first GPT I built (when OpenAI released GPTs) was called Life Coach — giving career advice and more. There’s still so much we’re discovering about how LLMs can be used — and plenty of risks to unpack along the way especially for that kind of use cases. But it does reveal something powerful: We’re not just building tools. We’re building companions. #AI #GenAI #FutureOfWork #EmotionalIntelligence

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It was great speaking with Dr. Vinesh Sukumar, VP of Product Management at Qualcomm, during Web Summit! In this clip, Vinesh shared key insights from his Masterclass on Edge AI, highlighting how #AI improves user experience through personalization, context, responsiveness, and privacy. Thanks for the chat, Vinesh and hope you found that pastel de nata!

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𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 𝗶𝘁 𝗺𝗲𝗮𝗻 𝘁𝗼 𝘀𝗰𝗮𝗹𝗲 𝗰𝗼𝗻𝘀𝘂𝗺𝗲𝗿 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀 𝗶𝗻 𝗮𝗻 𝗔𝗜-𝗳𝗶𝗿𝘀𝘁 𝘄𝗼𝗿𝗹𝗱? Dived deep into this at the roundtable at Bits n Atoms, hosted by The Product Folks last night — and the conversations stuck with me. Scaling consumer products in an AI world demands thinking across multiple dimensions, and these are just some of the lenses through which our group explored it.  • 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘀𝗮𝘁𝗶𝗼𝗻 — not just smart recommendations, but designing for one and actually impacting behaviour change  • 𝗧𝗿𝘂𝘀𝘁 — especially for this new generation of AI-native products, where UX becomes critical to adoption  • 𝗖𝗼𝘀𝘁-𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗻𝗲𝘀𝘀 — using AI to boost efficiency and also designing AI systems that can scale without exploding infra or model costs  • 𝗠𝗼𝗻𝗲𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻 — whether through subscriptions or new models that make sense for AI-first experiences  • 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 & 𝗚𝗧𝗠 — this differentiation matters more than ever, in a world where building products is getting faster and cheaper everyday  • 𝗔𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 — across regions, languages, devices, and modalities. If it only works for the few, does it really scale? We’re in an interesting moment — where product thinking is expanding, not just because of what AI can do, but because of the questions it’s forcing us to ask. Loved the sharp, honest, and wide-ranging perspectives from everyone Shreyanshi C. Kushal Khandelwal Ashish Jain Madhav Bhartia Tanmay Saxena Pranav Agrawal Sachin Kamkar Shashank Raghavendra Nikhil Ankan Huge shout out to Abhay Jani Aditya Mohanty Suhas Motwani for driving these thoughtfully curated and highly impactful events!

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1 week to go! Arvind and I are super excited to be hosting an AI evals hands-on workshop on February 15. It’s a 90-minute session where we’ll cover the fundamentals of AI evaluations and then actually do it together. Not theory only. You’ll walk away having built and reasoned through real evals yourself. If you’re aspiring to be an AI engineer or planning to upskill for AI roles in 2026, understanding how to evaluate LLM applications is critical. Building an LLM app is often the easy part. Knowing whether it truly works in production is where most teams struggle. LLM evaluation is not just about correctness anymore. You are judging usefulness, safety, faithfulness to sources, consistency across runs, cost, latency, and real user outcomes across prompts, RAG pipelines, tools, and agentic workflows. Because this is hard, many teams fall back to vibe-evals. If a few outputs look good, they ship. That approach breaks quickly when models change, prompts regress, retrieval fails, or user behavior shifts. Without evals, problems surface only after users complain. Why learning evals matters: ✦ Evals turn LLM development from guesswork into engineering ✦ They help catch regressions as models, prompts, or tools evolve ✦ They are essential for scaling RAG systems and agentic workflows ✦ In 2026, strong teams use hybrid evals combining automated metrics, LLM-as-a-judge, and targeted human review Evals are no longer optional. They are a core AI skill. In this workshop, Arvind and I will walk through how AI evals are done in real systems, with hands-on exercises so you build intuition. This is designed for AI engineers and tech leads building systems, and even business leaders who want intuition for what “good” really means in production. Even if you are a beginner, don't worry, we will give you the foundations for you to understand everything comprehensively, and enough resources that you can review at your own pace. If you want to level up your AI skills for 2026, register here: https://lnkd.in/dQSR7yhU

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Last month, we were building an AI agent for a client. It was giving close to 50 percent wrong answers in long chats. After fixing the context setup and memory flow, the mistake rate came down to under 5 percent. So what was the real problem? Problems we found • The session was not keeping the right parts of the chat • Key user info was lost after every turn • No process to store facts from past steps • No method to clean long chat history • The agent was pulling the wrong details into the model • No rule to update old info when new info came in What we fixed • A clean session flow with the right structure • Compact history so the model reads only the useful parts • A stable memory layer to store past facts • A simple way to extract important info from each turn • A clear rule to update old info and remove noise • A setup for safe and correct retrieval before each turn These changes made the agent stable, clear, and consistent in every long chat. If you want to learn how to set up context and memory in the right way, this new Google guide on Context Engineering, Sessions, and Memory is very useful. Repost so more builders can learn from this. Save it for later. Follow Shubham Vora for simple and real AI agent insights.

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Interested in how we test our systems at Amazon? In our new article in Communications of the ACM, Ankush Desai and I write about the evolution of systems correctness and testing practices at AWS. We cover a mix of classic approaches, formal approaches, and new techniques like deterministic simulation. https://lnkd.in/gwdncKM4

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"The gap between average and top agent products is widening rapidly. The fact that everyone has access to the same models means nothing." – Sandhya Hegde, AI luminary In her new essay, Sandhya argues that making AI products that work demands serious AI fluency. I see it as another confirmation that AI is "silk," not "linen": wrapping a little code around a model doesn’t guarantee a little value—it might deliver nothing. Sandhya breaks down design choices that drive drastically different outcomes - one agent or hundreds, chat-based or embedded or async. I find that architecture choices matters just as much. If you're trying to use AI to talk to structured data, results vary wildly depending on whether you: 1️⃣ Just point an LLM at a dataset and say “pretend you’re a Harvard-trained analyst” (spoiler: this doesn’t work) 2️⃣ Unify data in a platform like Data Cloud 3️⃣ Add RAG 4️⃣ Add a Knowledge Graph Builder teams now need AI fluency. Product or engineering, everyone must understand the model’s capabilities and experiment relentlessly to discover what works. It makes sense. After all, pretraining engineers spend weeks debating architecture — get it wrong, and you burn millions for nothing. Applied AI operates at a smaller scale, but it's not that different. What a time to be alive. Check out Sandhya's full essay here: https://lnkd.in/ghBZWfm8

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