Systems | Development | Analytics | API | Testing

Ep 28 | Engineering for GenAI: Lessons from Past Hype Cycles with Ryan Ries of Mission Cloud

Generative AI is entering the enterprise at scale, following patterns set by the rise of cloud computing. As organizations shift from buying tools to building custom solutions, architecture and integration become key to long-term success.

How to ensure your AI projects are production ready

Join Kong HQ for an insightful LinkedIn Live session titled "How to Ensure Your AI Projects Are Production Ready." As AI continues to transform industries, moving from experimentation to deployment is one of the biggest challenges organizations face. In this session, our experts will dive into what it truly means for an AI project to be "production ready," discussing essential practices around scalability, reliability, governance, and observability.

AI in Fitness: How Artificial Intelligence Helps FitTechs Grow?

Ever wondered if your fitness app actually knows you, or just logs your steps? Is it really smart enough, as smart as you think it is? Is it helping you train smarter, recover better, or just ticking off numbers on a screen? AI-powered fitness apps are quickly moving beyond basic tracking. They’re becoming intelligent companions, even those that understand your body, goals, and limits.

Beyond Banking: The Journey Into AI-Powered Customer Experiences

In this episode of the "Data Cloud Podcast," Dana Gardner, Principal Analyst at Interarbor Solutions, sits down with Ameesh Paleja, Executive Vice President of Enterprise Platform Technology at Capital One. They explore how Capital One leverages modern data architecture, automation, and AI to improve customer interactions and experiences. The conversation delves into the importance of standardized data, the role of AI-driven personalized services, and the integration of marketing and AI strategies.

Agentic RAG AI: Why It's the Future of BI Insights and Analytics Tools

If your BI and analytics tool isn’t powered by Agentic RAG AI, you’re missing out on advanced AI capabilities that enhance efficiency and visibility into data. Whereas agentic AI can work autonomously and without human intervention, RAGI AI combines the best information retrieval methods available with the power of AI. The result is deep knowledge of organizational data that improves over time.

Rethinking the Economics of Agentic AI: When 'Cheap' Gets Complicated

Everyone thinks AI is getting cheaper. But is it really? At first glance, the economics of AI seem to be improving for everyone. Thanks to continued model optimization and advances in hardware, the cost of running LLMs (also known as inference) is steadily decreasing. Developers today can access incredibly powerful models at a fraction of what it cost just a year ago. But there’s a catch.

When AI writes code that humans wouldn't: Testing in the age of agentic coding tools

Agentic coding tools like Cursor, GitHub Copilot, and OpenAI’s Codex are reshaping how software is developed. They enable developers to offload routine tasks and accelerate feature delivery. However, these tools also introduce new challenges – particularly in how we test and validate the code they produce.