From Models to Mission Control: Google Declares the ‘Agentic Era’
At Google Cloud Next, the company reframed its AI story around what it calls the “Agentic Era” — a shift from standalone models to fleets of autonomous cloud AI agents embedded in business workflows. Google says its first‑party models now process more than 16 billion tokens per minute via direct API calls, up from 10 billion last quarter, and over half of its overall machine learning compute investment this year is earmarked for the Cloud business. Building on that momentum, Google introduced the Gemini Enterprise Agent Platform, described as a mission control layer that links an organisation’s data, people and goals. Instead of just asking a chatbot for answers, enterprises are expected to spin up thousands of specialised agents that can be built, monitored and governed centrally. Internally, Google claims engineers already orchestrate “digital task forces” of agents, completing complex code migrations six times faster than before.
What Agentic AI Looks Like in Practice
Agentic AI, in Google’s framing, is less about a single, smart model and more about autonomous workflows composed of many specialised agents. These agents can plan multi‑step tasks, call tools, and hand work off to each other through what Google calls Agent‑to‑Agent Orchestration. The new Gemini Enterprise Agent Platform wraps these ideas into a product: an Agent Studio for low‑code development and central tools to scale, observe and govern agents. Google is also extending the concept into data and security. Its Agentic Data Cloud aims to turn passive data lakes into “systems of action,” letting agents reason across the full enterprise data estate in real time. On the defense side, Agentic Defense combines Google’s threat intelligence with Wiz’s platform to detect anomalies like “reasoning drift” or suspicious agent behaviour, while security operations agents already triage tens of thousands of unstructured threat reports each month, cutting mitigation times dramatically.
Inside TPU v8: Training and Inference Silicon for Cloud AI Agents
Underpinning this vision is Google’s eighth‑generation Tensor Processing Unit family, designed specifically for the demands of agentic AI. The TPU 8‑series introduces a dual‑chip strategy. TPU 8t is optimised for training large models and can scale up to 9,600 TPUs and 2 petabytes of shared high‑bandwidth memory in a single superpod. Google says it delivers three times the processing power of the Ironwood generation and up to twice the performance per watt, targeting ever‑larger model training runs. TPU 8i is tuned for inference, linking 1,152 TPUs in a pod with three times more on‑chip SRAM to drive massive throughput at very low latency — the kind of footprint needed to run millions of concurrent agents cost‑effectively. These TPUs sit alongside a growing portfolio of NVIDIA GPU instances, including upcoming Vera Rubin NVL72 systems, giving customers a mix‑and‑match hardware stack for training and serving agentic workloads.
Positioning in the AI Infrastructure Race
Google’s latest moves sharpen its position in the AI infrastructure race on two key fronts: custom accelerators and full‑stack agent platforms. With TPU v8 hardware, Google continues betting on vertically integrated silicon rather than relying solely on third‑party GPUs. The training‑focused 8t and inference‑focused 8i are explicitly tuned for agentic workloads, from large‑scale model building to latency‑sensitive orchestration of cloud AI agents. At the platform level, Gemini Enterprise Agent Platform and Agentic Data Cloud are Google’s answer to the industry shift from model hosting to end‑to‑end AI stacks that include data, orchestration, governance and security. While other hyperscalers also pitch their own accelerators and agent frameworks, Google’s approach leans heavily on being “customer zero”: internally, 75% of new code is now AI‑generated and approved by engineers, and marketing, security and operations teams already rely on Gemini‑based agents to accelerate work, providing real‑world validation for customers.
Implications for Enterprises: Power, Productivity and Lock‑In Risk
For enterprises, Google’s Agentic Era promises faster development, new application patterns and potentially lower compute costs — especially for large‑scale, always‑on AI services. TPU 8i’s focus on performance per dollar for inference and the ability to house millions of agents in a single pod are pitched as a way to make continuous, autonomous workflows economically viable. The Gemini Enterprise Agent Platform and Agent Studio lower the barrier for non‑specialists to build agents, while central governance and Agentic Defense tools aim to keep sprawling agent ecosystems compliant and secure. Yet the same tight integration that delivers efficiency also raises lock‑in questions. Adopting Google’s agentic stack — from Agentic Data Cloud through Gemini models and TPU v8 hardware — can yield strong performance, but makes it harder to lift‑and‑shift workloads later. As AI moves from pilots to mission‑critical systems, customers will have to weigh these trade‑offs carefully.