๐—ง๐˜„๐—ผ ๐—ฌ๐—ฒ๐—ฎ๐—ฟ๐˜€ ๐—™๐—ฟ๐—ผ๐—บ ๐—ก๐—ผ๐˜„, ๐—ง๐—ต๐—ถ๐˜€ ๐—ช๐—ถ๐—น๐—น ๐—•๐—ฒ ๐˜๐—ต๐—ฒ ๐—ข๐—ป๐—น๐˜† ๐—ฆ๐—ธ๐—ถ๐—น๐—น ๐—ง๐—ต๐—ฎ๐˜ ๐— ๐—ฎ๐˜๐˜๐—ฒ๐—ฟ๐˜€ ๐—ถ๐—ป ๐—”๐—œ

I spend my time building AI and talking to engineers who ship real products.

There is a huge gap between flashy demos and real production systems. People are not being honest about this gap.

Everyone calls everything an agent right now.

This mistake causes bad engineering. Teams spend weeks building complex orchestration for simple tasks. They should have used a single well-structured prompt.

Here is the truth: an agent is a system with an objective. It decides what to do next. It handles failure. It knows when it is finished.

Everything else is a fancy function call.

Successful teams do not chase new models. They focus on these three things:

The framework you use matters less than the patterns you follow. Patterns like "plan-then-execute" work in every framework.

I also see huge issues with RAG. Most people fail because their chunk boundaries are wrong. If you split a document poorly, the model loses context. It will hallucinate.

The fix is not a better embedding model. The fix is better chunking or using structured data.

Models will get better. Tokens will get cheaper. Context windows will grow.

None of that solves the core engineering problem. You must build systems that behave correctly when you are not watching.

The engineers who will lead in two years are not prompt engineers. They are systems designers. They build AI systems that other people can trust and maintain.

Do you agree? Tell me what you are building in the comments.

Source: https://dev.to/aibughunter/two-years-from-now-this-will-be-the-only-skill-that-matters-in-ai-4a9f