In this post, we present a classification of software applications in the current AI era across three distinct categories. Understanding these patterns is essential for technology leaders making architectural decisions — each demands different skill sets, governance models, and risk management approaches.
AI executes the core business logic and provides the output. Rather than being explicitly programmed via coding, the core logic is trained to solve problems through reasoning. Output accuracy varies depending on the maturity of the model, training data, and resource constraints. This category will become increasingly prevalent as we approach AGI. Troubleshooting is usually fine-tuning or retraining with different data.
This is currently the most prevalent category. It combines programmed logic working alongside AI systems, and best exemplifies the term "AI Native". It balances the AI thinking, reasoning, and decisions part with the action part using existing software tools.
It is these interactions between AI reasoning and software actions that led to the development of the Model Context Protocol (MCP). Troubleshooting usually involves just changing the prompts used.
With the advent of generative AI, "vibe coding" has become a reality where AI builds production-grade applications. AI conceptualises the required program logic and writes code much like a human developer. It started as a co-pilot for developers and has become so ubiquitous that development without it is competitively disadvantageous.
Applications built through this approach require thorough documentation and deep team understanding — inadequate code comprehension results in troubleshooting costs that far exceed the time savings from rapid generation. Troubleshooting requires understanding the full code.
Understanding these three distinct patterns is essential for technology leaders making architectural decisions. Each category demands different skill sets, governance models, and risk management approaches. The right choice depends on your problem domain, team capabilities, regulatory requirements, and tolerance for explainability trade-offs.
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