Cloud Computing

What Google I/O ’26 means for developing agents on Google Cloud

At Google I/O, we introduced a unified development toolkit featuring Antigravity 2.0 and the Managed Agents API, giving developers better ways to build locally and deploy securely to the cloud on a shared protocol layer. In this blog, we’re going to show you how Gemini Enterprise Agent Platform and the new developer tools shared at […]

What Google I/O ’26 means for developing agents on Google Cloud Read More »

Everything Google Cloud customers need to know coming out of Google I/O

At Google Cloud Next ‘26, we unveiled the blueprint for the Agentic Enterprise, sharing our eighth-generation TPUs, Gemini Enterprise Agent Platform, a fully reimagined Agentic Data Cloud, Workspace Intelligence, and security built for the AI era.  Today at Google I/O, we’re delivering a new set of powerful AI innovations and models — and putting them

Everything Google Cloud customers need to know coming out of Google I/O Read More »

The future of agentic development: Redefining the data practitioner lifecycle with Data Agent Kit

The modern software development landscape isn’t happening just on one surface — it’s happening across an entire ecosystem of agentic tools. Agents are being developed at an unprecedented scale, and these agents require direct access to enterprise data for context and grounding. However, the current tooling for building agents and managing data is heavily fragmented.

The future of agentic development: Redefining the data practitioner lifecycle with Data Agent Kit Read More »

The future of agentic development: Redefining the data practitioner lifecycle with Data Agent Kit

The modern software development landscape isn’t happening just on one surface — it’s happening across an entire ecosystem of agentic tools. Agents are being developed at an unprecedented scale, and these agents require direct access to enterprise data for context and grounding. However, the current tooling for building agents and managing data is heavily fragmented.

The future of agentic development: Redefining the data practitioner lifecycle with Data Agent Kit Read More »

How ALS GeoAnalytics LITHOLENS ™ revolutionizes core logging through machine learning with Amazon EKS

In the mining industry, accurate geological analysis is required for improving mine design and development. Traditionally, this involved labor-intensive and time-consuming on-site inspections of drill core samples, often conducted in remote and challenging environments. ALS GeoAnalytics has streamlined this process through its LITHOLENS platform, a machine learning (ML)-powered system that uses deep learning and machine

How ALS GeoAnalytics LITHOLENS ™ revolutionizes core logging through machine learning with Amazon EKS Read More »

How Synthesia optimizes generative AI video inference on Amazon EC2 G7e instances

Synthesia, an enterprise-focused AI video platform, has transformed content creation, helping everyone to create video content without cameras or microphones. To achieve this, Synthesia allows its users to create video avatars that synthesize the likeness and voice of real people. Synthesia achieves this through a series of in-house developed models based on various architectures, including

How Synthesia optimizes generative AI video inference on Amazon EC2 G7e instances Read More »

Beyond the Query: 5 Scenarios Laying the Foundation for the Agentic Era

Accessing enterprise data is shifting from static reports to dynamic use by autonomous systems. To keep up, organizations must route fragmented data from SaaS, IoT, and legacy sources into secure, scalable endpoints. However, moving to AI-driven exposure requires more than just connecting an LLM to a database, it requires a fundamental architectural shift to manage

Beyond the Query: 5 Scenarios Laying the Foundation for the Agentic Era Read More »

How Google Does It: Fleet-wide, large-scale A/B experimentation

When most people think of A/B experimentation, they think of button colors, landing page layouts, or checkout flows. At Google, many fundamental infrastructure improvements also need the rigor of A/B experimentation. Optimizing a memory allocator or a kernel scheduler can unlock massive savings in compute resources and slash latency for millions of users. But experimenting

How Google Does It: Fleet-wide, large-scale A/B experimentation Read More »