Browse past weeks of engineering reads.
How to deploy high-intelligence AI models with agentic capabilities to consumer hardware and mobile devices without requiring cloud infrastructure.
Enterprise systems need to react to events in real-time rather than relying on slow batch jobs or inefficient polling microservices that create dangerous delays in detecting critical issues like fraud or supply chain disruptions.
Organizations need to securely build, deploy, and govern autonomous AI agents at enterprise scale as the industry transitions from experimental LLMs to production agentic AI systems.
Automating the transformation of raw community signals into reliable technical guidance at scale using multiple specialized agents.
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.
How to enable developers to build multimodal AI agents that can process and respond to real-time audio, video, text, and generation capabilities beyond traditional text-based interfaces.
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.
AI agents built on Google Cloud need access to accurate, current, and grounded information about Google's products and APIs to function effectively.
Google needed to accelerate large-scale codebase migrations (TensorFlow to JAX) that are too complex and interconnected for manual developer effort or standard AI coding tools to handle efficiently.
Developers needed a unified, secure way to build AI agents locally and deploy them to Google Cloud with standardized protocols and tooling.