5 ways AI agents will transform the way we work in 2026
Today, Google Cloud dropped its 2026 AI Agent Trends Report.
Build and deploy scalable AI agents with NVIDIA NeMo, Amazon Bedrock AgentCore, and Strands Agents
This post is co-written with Ranjit Rajan, Abdullahi Olaoye, and Abhishek Sawarkar from NVIDIA. AI’s next frontier isn’t merely smarter chat-based assistants, it’s autonomous agents that reason, plan, and execute across entire systems. But to accomplish this, enterprise developers need to move from prototypes to production-ready AI agents that scale securely. This challenge grows as …
Bi-directional streaming for real-time agent interactions now available in Amazon Bedrock AgentCore Runtime
Building natural voice conversations with AI agents requires complex infrastructure and lots of code from engineering teams. Text-based agent interactions follow a turn-based pattern: a user sends a complete request, waits for the agent to process it, and receives a full response before continuing. Bi-directional streaming removes this constraint by establishing a persistent connection that …
You can now verify Google AI-generated videos in the Gemini app.
We’re expanding our content transparency tools to help you more easily identify AI-generated content. You can now check if a video was edited or created with Google AI d…
Inside Kaggle’s AI Agents intensive course with Google
Kaggle’s AI Agents Intensive with Google brought learners together in a no-cost course to build and deploy the next frontier of AI.
Watch a podcast discussion about Gemini 3 and the future of Search.
Learn how Gemini 3 powers Google Search with Generative UI, Nano Banana, and interactive graphics.
Tracking and managing assets used in AI development with Amazon SageMaker AI
Building custom foundation models requires coordinating multiple assets across the development lifecycle such as data assets, compute infrastructure, model architecture and frameworks, lineage, and production deployments. Data scientists create and refine training datasets, develop custom evaluators to assess model quality and safety, and iterate through fine-tuning configurations to optimize performance. As these workflows scale across …
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Track machine learning experiments with MLflow on Amazon SageMaker using Snowflake integration
A user can conduct machine learning (ML) data experiments in data environments, such as Snowflake, using the Snowpark library. However, tracking these experiments across diverse environments can be challenging due to the difficulty in maintaining a central repository to monitor experiment metadata, parameters, hyperparameters, models, results, and other pertinent information. In this post, we demonstrate …
Gemini 3 Flash: frontier intelligence built for speed
Gemini 3 Flash offers frontier intelligence built for speed at a fraction of the cost.
Governance by design: The essential guide for successful AI scaling
Picture this: Your enterprise has just deployed its first generative AI application. The initial results are promising, but as you plan to scale across departments, critical questions emerge. How will you enforce consistent security, prevent model bias, and maintain control as AI applications multiply? It turns out you’re not alone. A McKinsey survey spanning 750+ …
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