The tech world is grappling with a paradox: the growing demand for complex software modernization collides with the current limitations of AI development tools. Companies are increasingly focused on rewriting critical systems without pausing their operations, a task that requires precision and reliability. This trend of continuous modernization, as highlighted by recent discussions, means businesses cannot afford disruptions. However, the current state of AI in development, particularly with coding agents, presents significant hurdles. It's clear that AI agents often fail in non-obvious ways. Issues range from commands not running to repeated actions, sudden cost spikes, or confused answers, far beyond what a simple prompt adjustment can fix. This suggests that while AI promises efficiency, its current debugging challenges can negate those benefits, especially when dealing with live systems. Meanwhile, user experience with AI features in consumer products like mobile operating systems reveals a similar pattern. Recent polls show that new AI features are often just 'okay' rather than revolutionary. While adequate for daily use, this level of performance isn't sufficient for the high-stakes environment of enterprise software development. Looking ahead, we predict a push for more robust and transparent AI development tools. The simple act of 'tweaking the prompt' will no longer be enough. Developers will demand better insights into how AI coding agents make decisions, consume resources, and interact with tools. We will see a shift towards debugging tools that reveal the internal workings of AI, showing precisely why a tool call failed or why an agent got stuck. This evolution is crucial if AI is to truly assist in non-disruptive software rewrites and move beyond being merely 'okay' to becoming an indispensable, reliable partner in solving complex business problems.