Biology, Economics, and Computation All Point to Agentic Workflows The push toward AI agents might be telling us something about the limits of LLMs. Every time we look at how intelligence evolves - whether in nature or human society - we see the same pattern. Things start general, then inevitably split into specialized roles. We saw it when human societies evolved from hunter-gatherers into specialized trades. What's fascinating is how we're seeing the same story play out in AI development. We started with specialized AI systems, then got excited about building massive do-everything models, and now we're discovering that maybe we need specialized AI agents after all. It's the difference between training a general practitioner to be a world-class surgeon versus just training a surgeon in the first place. The economics make this even more interesting. Sure, we can keep scaling up language models, but the computational costs are getting ridiculous. Meanwhile, specialized agents can do specific tasks better with a fraction of the resources. Here's where it gets tricky though: Having specialized agents creates coordination challenges. You're trading one type of complexity for another. Instead of the challenge being "how do we make this AI understand everything," it becomes "how do we get these specialized AIs to work together effectively." To me, this suggests the future of AI might look less like a single superintelligent brain and more like an ecosystem of specialized agents. Not because we couldn't theoretically build the former, but because the latter might just be a more practical and efficient way to solve problems. *Originally published [here](https://x.com/barusebi/status/1860133343947301114)*