Nothing here yet.
Nothing here yet.
Completely agree, most failures I’ve seen come from poor context management and unclear data flow, not the model itself. State handling also becomes a major issue when workflows scale, especially with multiple tools and agents interacting. In my experience, debugging improves a lot once you treat it as a system design problem rather than just an AI model issue.
Most companies are still in the “AI-flavored features” stage rather than building truly AI-native products. Adding chatbots or automation layers is easier and quicker than redesigning products around AI from the ground up. AI-native products require rethinking workflows, data architecture, and user experience with AI at the core not just as an add-on. That said, we’re starting to see a shift as companies realize that real competitive advantage comes from deeply integrated AI capabilities, not surface-level features. The transition is happening, but it’s still early for most organizations.