Recorded on the floor of LDX3, Ryan chats with Nivia Henry, an engineering leader most recently at Netflix, about the reality of the supposed SaaS apocalypse, the complex calculus of the modern build-versus-buy decision in the era of AI, and the critical importance of understanding system architectures to manage long-term infrastructure costs.
Rumors of the SaaS demise are greatly exaggerated Read More »
Every organization has unique requirements for building and scaling generative AI. At Google Cloud, we are committed to giving developers model choice and architectural flexibility to run workloads however they need. Today, we’re announcing the general availability of an Anthropic-managed deployment option for Claude Models on Google Cloud. This gives teams more choice in how
Expanding Claude availability and deployment choice on Google Cloud Read More »
Today, we’re launching self-learning context enrichment (preview) and a reimagined Knowledge Studio experience (preview) in Knowledge Catalog, Google Cloud’s context engine for agents. Building on Knowledge Catalog’s existing Gemini-powered enrichment, self-learning enrichment analyzes agent and user interactions to find gaps and keep your business context accurate and current. Knowledge Studio is the new command center
Introducing new self-learning context enrichment in Knowledge Catalog Read More »
Today, we’re launching self-learning context enrichment (preview) and a reimagined Knowledge Studio experience (preview) in Knowledge Catalog, Google Cloud’s context engine for agents. Building on Knowledge Catalog’s existing Gemini-powered enrichment, self-learning enrichment analyzes agent and user interactions to find gaps and keep your business context accurate and current. Knowledge Studio is the new command center
Introducing new self-learning context enrichment in Knowledge Catalog Read More »
Every decade or so, a shift in software forces us to rethink computer architecture from first principles. After the first boom of web-scale traffic, Google pioneered warehouse-scale computing and Linux containers on Borg. This reshaped how we run internal workloads and ultimately sparked the modern cloud-native ecosystem through Kubernetes. A decade later, when deep learning
Inside Agent Compute Engine: Building the compute foundation for the agentic era Read More »
Blog authors: Nirav Mehta, VP Product Management; Michael Bachman, VP/GM; Kevin Hutchins, VP Product Management Scaling Gemini to planet scale taught us firsthand that securely running autonomous agents is fundamentally an infrastructure challenge. The shift from single-turn prompting to interactive agents requires a new paradigm of execution and control. To support massive concurrent agent sessions,
Introducing Agent Compute Engine and expanded agent governance Read More »
With AI agents, you can automate medical coding while keeping the workflow deterministic, auditable, and subject to human approval. The key is division of labor: the workflow engine owns sequencing and approval gates, while agents reason within individual, bounded steps. Handing the entire workflow to an agent would make the runtime path a property of
Automate medical coding with Step Functions and Amazon Bedrock AgentCore Read More »
For data science, data engineering, and machine learning teams, unlocking the value of unstructured data, such as images, videos, audio, and documents, has historically meant managing siloed data pipelines for structured and unstructured data, and for analytics and AI/ML use cases. Even when files remain in Cloud Storage, running inference requires teams to orchestrate external
