You might be surprised by this, but it's worth revisiting: more agents don't automatically equal better solutions. This is a fundamental misconception that keeps surfacing in AI discussions.

We've seen this pattern before. Two decades ago, the CPU industry learned the hard way that core count alone doesn't drive performance—optimization and architecture matter far more. The same principle applies to multi-agent systems today.

The real answers rarely come from academic papers alone. What actually moves the needle? Iteration. Experimentation. Failing, learning, trying again. That's where breakthroughs happen. The gap between theory and practice in agent design is wider than most realize, and only hands-on testing closes it.

If you're tackling similar challenges in agent deployment or distributed systems, the path forward isn't scaling blindly—it's understanding what you're optimizing for.