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Why Hybrid Agents May Beat Bigger Models

8 September 2025 · 113 views · 1 likes · 0 reposts · 112 blog views

AI automation learning productivity research

2025 feels like the year of agents. Everywhere I look, people talk about them... these agents can call tools in the background for you (an agent can open your email, grab all the links, research it, etc.)

Big models are smart enough to cover for messy tools. They can guess, recover, and brute-force their way through bad interfaces. Small models (SLM) can’t. Which means you need to build better tools and handle errors properly.

The big models make more mistakes when chained together. Even if each step is 95% correct, the errors stack up until the whole thing collapses. Small models force you to design clean, deterministic steps.

The future of agents isn’t just about bigger models. It’s about smarter architecture (see NVIDIA paper on that). Local small models handle the bulk of tool calls, and large ones are responsible for complex reasoning. That hybrid setup keeps costs near zero per call and still gives you power when you need it.

This is something I’ve started testing in my projects, and it seems like a very interesting direction.

Why Hybrid Agents May Beat Bigger Models

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