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Product-first workflows reverse traditional AI development. Begin with what users need, prototype quickly using existing models, iterate based on actual feedback. Teams measure tangible outcomes—problems solved, time saved, decisions improved. Technical sophistication becomes a means, never the end. The question shifts from "what can this model do" to "what should this product achieve."

Agents communicate through schemas and typed function calls, making entire systems testable. Models reason freely inside their boundaries, then translate to structured outputs before touching production systems. This separation preserves flexibility where it helps while enforcing reliability where it matters. Architecture brings order to probabilistic behavior.

AI handles the volume—millions of data points processed in seconds. Humans handle the nuance—asking better questions, interpreting context, making calls that require wisdom. Augmented intelligence means technology expands what people can do rather than replacing what they know. The collaboration preserves human judgment while removing drudgery.

The agent economy rewards robust, observable, composable stacks over large language models. Organizations treating infrastructure as strategic gain speed and resilience. Performance depends on how components connect, how failures surface, how systems adapt. The best automation becomes boring—reliable enough to forget about, powerful enough to transform operations.

One giant agent creates unmaintainable spaghetti logic. Break systems into role-specific components instead—distinct layers for perception, reasoning, planning, memory. Modular architecture makes failure modes visible and fixable. You can measure each component independently, improve specific capabilities, understand exactly where breakdowns occur. Complexity becomes manageable through separation.