I just wrapped up a half-day AI Agents Live + Labs workshop where Google Cloud walked us through the entire agent lifecycle—from prompt design to production deployment—using their newest agentic tooling. The pace at which ideas became running agents was eye-opening, and the lab format made each concept stick.
Gemini 2.5 Pro sets a new bar. Its 1M-token context window and native multimodality now top industry benchmarks for code generation, long-form reasoning, and multilingual tasks.
Agentspace gives enterprises a single pane to deploy, discover, and govern domain-specific agents—think customer support, security, or data science—backed by Google-quality search.
Agent Garden (part of Vertex AI Agent Builder) offers a library of pre-built agents and tools that dramatically shortens the path from idea to PoC.
AI-optimized infrastructure is ready for anything: A3 and A4 VMs with NVIDIA H100 and B200 GPUs plus Trillium and Ironwood TPUs span training through real-time inference at teraflop scale.
Security & governance by design. Role-based access control, VPC Service Controls, built-in guardrails, and evaluation tooling are baked into Agentspace and Vertex AI to keep multi-agent systems compliant and trustworthy.
Google Cloud isn’t just chasing the agent trend—it’s packaging everything you need (models, agent frameworks, infra, and guardrails) into one cohesive platform. For teams under pressure to ship agentic features quickly, this means less duct-taping and more building.
With Gemini 2.5 Pro’s reasoning power, Agentspace’s orchestration, and the raw muscle of H100/B200 GPUs, turning an idea into a production-grade agent is now a weekend project—not a quarter-long initiative. I’m already sketching how to plug Agent Garden samples into our existing Vertex AI pipelines to automate customer-support triage and data-ops playbooks.
Bottom line: if you’re evaluating platforms for large-scale, secure multi-agent systems, Google Cloud just made the short list. Time to roll up your sleeves and start experimenting!