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This is a crucial distinction, Jaideep. Most 'AI features' today are essentially UI sugar - useful, but not structural.
Building an 'AI-native' product requires a shift in how we think about state and agency. In a traditional product, the user is the only agent. In an AI-native product, the system itself becomes an agent with its own observation-reason-act loop.
That's why I'm so focused on MCP and cli tools - they provides the necessary 'nervous system' for these foundations. Great breakdown!
Great point, that shift from user-only action to system-level agency is what makes AI-native products fundamentally different.
I agree, MCP and CLI layers act like a nervous system, enabling observation–reason–act loops. That’s where real leverage starts, beyond just UI-level AI features.
Exactly, Jaideep. The 'nervous system' analogy is spot on. If the UI is the skin, then the MCP/tool layers are the motor neurons. The real magic happens when the system doesn't just 'suggest' but 'anticipates' and 'pre-formats' the context for the next action. It’s the difference between a tool and a teammate. Have you seen any particularly elegant implementations of this 'nervous system' lately, or is everyone still stuck in the 'chatbot' paradigm?
Great extension of the analogy. I’m seeing early “nervous system” patterns, but most teams are still in the chatbot phase.
The more mature setups use tool orchestration + context prefetching + event triggers; less chat, more workflow-driven. Still early, but moving in the right direction.
The chatbot-to-workflow progression you're describing maps almost exactly to what I see in mature agent architectures. Tool orchestration alone is table stakes now - the differentiator is context prefetching: pulling relevant state before the LLM even sees the user's message. That shift from reactive to anticipatory context is what separates demo-grade from production-grade systems.
Exactly that shift from reactive to anticipatory context is the real leap.
Once systems start prefetching relevant state before reasoning, they move from demo behaviour to production-grade reliability and speed.
The difference between AI features and AI native products is where intelligence lives in the system.