Agent Plugins package your skills, tools, and more
Developers had to maintain separate wrappers and configurations to make Agent Skills and MCP servers compatible with different AI coding agents and IDEs.
Model routing with Google Cloud API Gateway
Developers needed a way to dynamically route API requests to different LLM providers (Gemini, Claude, OpenAI) without hardcoding endpoints or managing custom proxy infrastructure.
Scaling AI Agent Infrastructure with the MCP Stateless updates
AI agent infrastructure needed to scale horizontally and support long-running interactive tasks without being constrained by stateful architecture patterns.
Scaling real-time AI agents with session-aware load balancing
Real-time AI agents with long-lived stateful bidirectional streams cannot be effectively load-balanced using traditional request-response metrics because server capacity is obscured by the nature of continuous conversations.
Agent and Model Evaluations in Gemini Enterprise Agent Platform are now GA
Developers lack a consistent, standardized way to measure and evaluate the quality of AI agents across both local development and production environments.
Enable on-demand expertise with Agent Skills in Genkit Go
Preventing context window bloat and excessive token consumption when agents need access to specialized instructions and references.
How to use Google microbenchmarks for evaluating TPU performance
Engineers need a systematic way to diagnose performance bottlenecks in TPU-based machine learning workloads to understand whether they are constrained by compute, memory, or network resources.