With hundreds of models to choose from, how do you pick a stack that holds up in a live conversation? Most agents work in the background, but the moment one has to hear, see, and speak with a human in real time, everything about how you build and evaluate it changes. I'll show why public leaderboards fail for realtime agents, and why the latency on your dashboard isn't the latency your users feel. Then I'll define the outcomes you want as human-equivalent behaviors, and work backwards to your evals, your models, and a production iteration loop. You'll leave with a concrete decision framework and an open benchmark you can run on your own stack.
