Lior Gavish on AI Agents Podcast: Data + AI Observability and Reliability

Building Reliable AI Systems at Scale in the Era of LLMs and SLMs In Episode 162 of the AIAW Podcast, we’re joined by Göran Sandahl, Co-Founder of Opper AI, to explore the future of building reliable AI systems at scale. Göran shares his journey from observability engineering to co-founding Opper AI, a company tackling one of the most urgent challenges in enterprise AI: making LLM-based features predictable, testable, and production-ready. We dive into Opper’s structured API approach, the strategic vision behind their recent acquisition of FinetuneDB, and how combining dynamic prompt engineering with fine-tuning workflows unlocks new levels of reliability for AI agents. From “specification-first” design to real-time tracing, Göran offers a sharp look into how developers can build AI systems that don’t just work—but can be trusted. A forward-looking conversation on what it takes to build harmony between humans and machines, one integration at a time. https://lnkd.in/dPsfndab

Learn more about context engineering and many other things relevant for Enterprise Grade AI. One of the key topics at the root of building AI systems and AI agents are repeatabillity of results to trust things in production at scale. On the most fundamental level so are MANY so called AI experts/influencers totally missing the point that AI systems are inherently Probebalistic Systems or have probebalistic features. This is foundational math. Yet we go at it with Determinstic understanding and with knowhow HOW we control and steer a probibalistic system as a blindspot. And then we blame the AI for our stupidity/ignorance what this means. It means a different way to build systems. To reap the benefits of probibalistic systems we also need to build to mitigate the risks/ backside of using probebalistic systems. Compliance and Risk management by design consequently ALWAYS need AI to be viewed as an AI compund system with built in guardrails to steer probebalistic functions. ANY AI literacy or AI training need to start to tie into or current Determinstic Worldmodels, governance and organisation and give understanding and educate us how we reform towards things that are based on probebalistic features. All roles need some basics fundament to start from to better understand the nature of the trajectory we are on. Start with listening to THIS pod. If the above is a blindspot in your point of view on AI. Dairdux #dataaicamp

In this episode of Beginner’s Guide to AI, Matt Hicks, CEO of Red Hat, unpacks why the future of business strategy in AI mirrors the age of the railroads. Just as railroads transformed industries, AI is laying down the tracks for the next wave of innovation — and businesses must decide whether to board or be left behind.

🤖 AI is no longer just a tool, it’s becoming a new species we’ll have to coexist with. But who decides the rules it follows? Craig Mundie, former Microsoft Chief Research and Strategy Officer and co-author of Genesis with Henry Kissinger and Eric Schmidt, argues AI will be like a new species and we can’t leave the rules up to each developer. Would we let each driver make up their own traffic laws? Society needs to step in, define boundaries, and guide AI responsibly. The future of humanity might depend on it. 💬 Do we need global rules for AI? Drop your thoughts below. 👉 Learn more in this conversation on 3 Takeaways ✅ Read the free 3 Takeaways LinkedIn newsletter https://lnkd.in/ejUt-vDh ✅ Tune in now on 3 Takeaways, the top 1% global podcast https://lnkd.in/e-YA53iC #PodcastRecommendations #3Takeaways #ArtificialIntelligence #FutureOfAI #AIRevolution #TechInnovation #NextGenTech #MachineLearning #AIImpact #AITech #AIThoughtLeadership #InnovationMatters #CraigMundie

In finance and operations, AI is only as effective as the data that powers it. Clean, connected, and contextual data drives predictive accuracy, operational efficiency, and smarter decision-making. From optimizing financial models to streamlining business workflows, data enables AI to turn complexity into clarity and foresight into action. The stronger the data foundation, the sharper the AI outcomes. Watch this byte from Tech Talks with Kapil Mehta, hosted by Romil Dodhiwala #TechTalks #AI #DataDriven #Finance #Operations #DigitalTransformation #ArtificialIntelligence #DataAnalytics #Podcast

Two CTOs. One big question: how are AI and data transforming the enterprise?   Rajesh Indurthi (CTO, Charter Global) and Nevarda Smith (CTO and CAIO), MagMutual) kick off The Data Shift, our upcoming podcast exploring the intersection of Artificial Intelligence, data, and digital transformation.   In this premiere conversation, they share real-world insights on what truly drives impact, from navigating messy data realities to building AI systems that scale and stick.   The Data Shift is coming soon. Stay tuned for candid stories, hard-won lessons, and playbooks you can take back to your team. #TechLeadership #EnterpriseTransformation #AIandData #LeadershipDialogue #DigitalInnovation #TheDataShift

Charter Global it was a great discussion that explored the needs for AI projects to have Strategic Alignment, ROI, Governance, Ethics, Data Readiness, Change Control, and limiting Vendor Shinny Object Driven AI from distracting the big wins with aligning with the right frameworks, tools, and strategic vendor assessments.

Will AI replace us, reshape our work, or create opportunities we’ve never imagined? In this episode of Harvard Data Science Review, HBS Professor Raffaella Sadun and data scientist Ben Waber cut through the hype to explore AI’s real impact on jobs, skills, and organizations. They examine myths of full automation, the need for firm-level experimentation, and the evolving management strategies required in the age of AI.

AI is not just a technological wave; its a tool shaping the future of business leadership, economic inclusion, & global collaboration. #GlobalBusinessLeadersNetwork #GBLN #AI #Business

Interesting and fresh perception from Andrej Karpathy. The OpenAI cofounder said on the Dwarkesh Podcast that: functional AI agents are at least a decade away. Why? They're just not ready. They can't maintain context across sessions. They make assumptions instead of asking questions. They lack true multimodal capabilities. But here's what caught my attention: Karpathy isn't worried about AI replacing programmers. He's worried about programmers getting lazy. His vision isn't autonomous agents churning out mountains of code while humans sit back. It's humans and AI working together – where the AI pulls up API docs, asks clarifying questions, and helps you become a better developer in the process. The industry is building for a future where humans are obsolete. Karpathy wants to build for a future where humans are amplified. #AI #AIAgents #ArtificialIntelligence #TechIndustry #FutureOfWork #SoftwareDevelopment #TechLeadership #Innovation #AIethics