Has NVIDIA Hit the Nail on the Head with Groq 3 LPX for AI Agents?

I’ve been thinking about something with the shift from chatbots to AI agents. For a chatbot, waiting an extra second or two for a response is annoying. But an agent might need to: Reason → call a tool → receive result → reason again → call another tool → generate response If that happens 10–20 times, latency can compound pretty quickly. This makes me wonder: Is inference latency actually becoming one of the biggest bottlenecks for production AI agents? We’ve traditionally focused heavily on: model intelligence context windows token cost accuracy tool calling But perhaps tokens/second and time-to-first-token become much more important as agents become more autonomous. NVIDIA’s new Groq 3 LPX is an interesting development in this direction, specifically targeting low-latency inference for agentic workloads. But I’m curious what people actually see in practice: For production agents, what’s the bigger problem today besides cost - latency, tool/API latency, reliability, or something else? And does faster inference actually change what kinds of agent architectures are practical?