The recent Cloud Next '26 event marked a decisive shift in how organizations conceptualize machine intelligence. While the conversation around AI frequently centers on chatbots or image generation, Google is quietly pivot-ing toward the infrastructure layer, focusing on 'agentic' workflows—systems where AI doesn't just chat, but performs complex, multi-step tasks across professional environments.

At the heart of this strategy is the Gemini Enterprise Agent Platform. It functions as a central command center for companies to build, govern, and scale AI agents that act autonomously. The numbers are striking: Google's first-party models are now processing over 16 billion tokens per minute. For context, this massive throughput represents the backbone of the next industrial phase, where digital agents act as the connective tissue for enterprise operations.

Perhaps most compelling is Google’s own transition into 'Customer Zero.' Internal data from the company reveals that 75% of its new code is now AI-generated, a leap from 50% just six months ago. By deploying agentic workflows, engineering teams completed complex code migrations six times faster than manual processes allowed. This isn't just about efficiency; it's about re-architecting the human-machine collaboration loop, enabling developers to prototype native apps in days rather than months.

To support this scale, Google is rolling out its eighth-generation Tensor Processing Units (TPUs). The hardware split is telling: the TPU 8t is designed for massive training, boasting 2 petabytes of shared memory in a single superpod, while the TPU 8i is optimized specifically for inference. By reducing latency with specialized on-chip memory, the 8i pod enables millions of agents to run concurrently. This hardware-software synergy ensures that the agentic era isn't just a theoretical promise but a functional, performant reality for global enterprises.

The recent Cloud Next '26 event marked a decisive shift in how organizations conceptualize machine intelligence. While the conversation around AI frequently centers on chatbots or image generation, Google is quietly pivot-ing toward the infrastructure layer, focusing on 'agentic' workflows—systems where AI doesn't just chat, but performs complex, multi-step tasks across professional environments.

At the heart of this strategy is the Gemini Enterprise Agent Platform. It functions as a central command center for companies to build, govern, and scale AI agents that act autonomously. The numbers are striking: Google's first-party models are now processing over 16 billion tokens per minute. For context, this massive throughput represents the backbone of the next industrial phase, where digital agents act as the connective tissue for enterprise operations.

Perhaps most compelling is Google’s own transition into 'Customer Zero.' Internal data from the company reveals that 75% of its new code is now AI-generated, a leap from 50% just six months ago. By deploying agentic workflows, engineering teams completed complex code migrations six times faster than manual processes allowed. This isn't just about efficiency; it's about re-architecting the human-machine collaboration loop, enabling developers to prototype native apps in days rather than months.

To support this scale, Google is rolling out its eighth-generation Tensor Processing Units (TPUs). The hardware split is telling: the TPU 8t is designed for massive training, boasting 2 petabytes of shared memory in a single superpod, while the TPU 8i is optimized specifically for inference. By reducing latency with specialized on-chip memory, the 8i pod enables millions of agents to run concurrently. This hardware-software synergy ensures that the agentic era isn't just a theoretical promise but a functional, performant reality for global enterprises.