Beyond Big Models: The New AI Bottleneck

JUST IN: While everyone's focused on scaling AI models, the real bottleneck might be elsewhere. It's not just about making things bigger, but making them smarter. We're talking about 'scaling the harness', a term you'll be hearing a lot more of. It means treating the structured execution layers around foundation models as a critical part of the design process, not just an afterthought.

Why Harness Matters

Let's break it down. Recent language models have shown they can fetch data, use tools, and manage tasks over time. But current evaluations focus too much on model-centric metrics. We need to look at how everything works together, memory, context, skill routing, and governance.

Why should you care? Because this interaction is where agent performance really comes from. It's the combination of the model, memory banks, context builders, and skill-routing mechanisms. Together, these elements form what's being called the 'agent harness'. The key is turning raw model power into practical, effective agent behavior.

The Real Challenges

So, what's holding us back? Three main hurdles: context governance, reliable memory, and dynamic skill routing. It's not just about adding these layers, but orchestrating them. This coordination is what will push AI beyond its current capabilities.

The labs are scrambling to figure this out. New benchmarks need to measure more than just task success. They should evaluate trajectory quality, memory management, and overall system efficiency. It's not just about succeeding once, it's about safe, consistent evolution over time.

Meet CheetahClaws

Enter CheetahClaws. It's a Python-native reference harness designed to tackle these challenges head-on. Compared to Claude Code and OpenClaw, it's leading the pack in making these abstract concepts more concrete.

Here's the kicker: Future AI progress might depend more on system design than on beefing up foundation models. Are we at the end of the 'bigger is better' era? Seems like it. The shift to harness-level thinking could redefine our path forward.

And just like that, the leaderboard shifts. Will the labs get it right? Or is this another buzzword we’ll forget about next year?