TPUs for the Agentic Era: Hardware Finally Catching Up to the Workload Google's announcement of two new TPU variants — the 8T for training and 8I for inference — isn't just another hardware refresh. It's an admission that the workloads we've been throwing at AI infrastructure have outgrown the general-purpose designs we've been using. The agentic era demands something different. The Mismatch We've Been Ignoring For the past two years, we've been building agents that reason, plan, and execute across multiple steps. Each agent loop involves inference, tool calls, context retrieval, and state updates. Yet we've been running these workloads on hardware optimized for batch training jobs — massive parallel matrix multiplications with predictable memory access patterns. Agentic inference looks nothing like that. It's bursty, latency-sensitive, and memory-bandwidth constrained. Context windows balloon. KV caches fragment.…