The Tech Blog Writer Podcast - Inspired Tech Startup Stories & Interviews With Tech Leaders, Entrepreneurs And Innovators

How do you measure success when your AI is learning faster than your own business processes can keep up? That’s the question I set out to answer in my conversation with SparkBeyond, a company that has spent the past decade transforming how enterprises harness AI. From crawling GitHub code in a modest garage experiment to driving measurable performance gains for global firms, SparkBeyond has charted a path that mirrors the rapid evolution of AI itself. In this episode, I explored how their focus has shifted from discovering hidden performance drivers in customer data to building agentic AI systems that actively close feedback loops and optimize themselves continuously. SparkBeyond brings the rigor of operational excellence into the world of AI agents, a space still notorious for inefficiencies and inconsistent results. Agentic AI isn’t just the next shiny term; it represents a practical step forward from passive prediction to autonomous decision-making. Listening to examples like automated troubleshoo