Browse past weeks of engineering reads.
AI applications that work perfectly in local development environments fail when deployed to production in enterprise settings due to infrastructure constraints, cascading errors, and organizational governance barriers.
AI coding assistants consume excessive tokens due to context bloat, causing increased latency, higher costs, and reduced model accuracy.
Protecting proprietary AI models and applications deployed at enterprise scale on Kubernetes while defending against novel threats like prompt injection and maintaining regulatory compliance without impeding developer velocity.
Enabling efficient GPU and TPU resource allocation and management in Kubernetes clusters while abstracting infrastructure complexity from users.
Bridging the gap between rapid AI prototype development and production-grade AI agent applications that meet enterprise reliability and performance standards.
Enterprise generative AI agents cannot efficiently scale to handle hundreds of heterogeneous data structures, dynamic business rules, and shifting API schemas without hardcoding all tool definitions into static system prompts.
Enterprises need visibility and diagnostics across multi-cloud and hybrid network environments where applications span Google Cloud, on-premises, AWS, Azure, and internet services, making it difficult to identify the root cause of performance degradation.
Ensuring high availability and service continuity when AI inference workloads fail in one region while maintaining access to the service across multiple regions.
Moving AI agents built with Google's Agent Development Kit from local prototypes to production-ready, scalable infrastructure.
Managing startup latencies up to 20 seconds for AI workloads on Cloud Run serverless GPUs, which causes poor user experience and is driving developers back to traditional container orchestration.
Deploying and managing AI agents at scale in production requires infrastructure for state management, security governance, and complex workflow orchestration that goes beyond demo implementations.
Google Cloud needed to bridge the gap between high-level keynote announcements and practical implementation details that developers could immediately apply.
BASF needed to manage and optimize thousands of interdependent supply chain decisions across 180 global production sites where weather and regulatory changes can cause cascading disruptions in a two-year production pipeline.
Building safe, reliable, and autonomous agents that can act independently across multiple enterprise systems while maintaining security, governance, and reliability guardrails.
How to help developers transition from understanding AI concepts to building and maintaining production agentic systems in cloud environments.
Organizations need to secure their AI systems and infrastructure against emerging AI-era threats while maintaining the ability to leverage AI's potential at scale.
Development teams struggle to safely deploy code to production while managing the risk of releasing features to all users simultaneously, especially as AI accelerates code generation faster than safe deployment practices can keep up.
Enabling seamless connectivity, governance, and security across multi-agent AI systems and core applications distributed globally at planet scale.