Building AI Agents with Google Cloud: Powering Innovation with Langgraph and Vert.x AI

- Authors
- Published on
- Published on
In this thrilling episode of Google Cloud Tech, we delve into the exhilarating world of building AI agents with the powerhouse that is Google Cloud. These AI agents are no ordinary applications; they possess the ability to reason, plan, and execute tasks with the finesse of a seasoned race car driver. Utilizing language models, memory, and context sources, these agents interact with users synchronously or asynchronously, providing a dynamic user experience that puts them in the driver's seat. But hold on to your seats because the excitement doesn't stop there.
Enter Cloud Run, the high-octane runtime that propels AI agents to new heights on Google's scalable infrastructure. With automatic scaling, cost-effective pricing, and enterprise-grade security, Cloud Run is the ultimate pit stop for developers looking to unleash the full potential of their AI creations. But the adrenaline rush doesn't end with Cloud Run; we shift gears to explore Langgraph, a cutting-edge framework that combines the reliability of structured workflows with the flexibility of agents. This is where the rubber meets the road, allowing developers to navigate the twists and turns of AI development with precision and agility.
But wait, there's more. Buckle up as we accelerate into the world of Vert.x AI, where intelligence meets innovation to power the next generation of AI agents. With access to Google's Gemini models, open-source models, and seamless integration with enterprise data sources, Vert.x AI is the turbo boost that agents need to reach peak performance. The Vert.x AI agent builder is the command center, offering developers a suite of tools to build, deploy, and discover agents with ease. From the agent development kit to the agent engine, Vert.x AI is the ultimate race track for AI innovation, where speed, reliability, and flexibility converge in a thrilling display of technological prowess. So rev up your engines and join us on this adrenaline-fueled journey through the fast-paced world of building AI agents with Google Cloud.

Image copyright Youtube

Image copyright Youtube

Image copyright Youtube

Image copyright Youtube
Watch Building AI agents on Google Cloud on Youtube
Viewer Reactions for Building AI agents on Google Cloud
Excitement about learning more about Vertex AI and its capabilities
Suggestions for improving audio quality in the video
Confusion between using Vertex AI Python library or Google ADK library
Limitations of deploying to "Agent Engine" with remote ArtifactService
Request for sharing assistance code with multi-agent
Interest in working on the project
Question about the difference between Cloud Run and Vertex (runtime vs. design tool)
Note about incorrect links in the resources
Difficulty in building an agent as mentioned in the video
Concern about the cost of learning AI for engineers from non-AI backgrounds
Suggestion to separate topics of LangGraph/CloudRun and Vertex AI Agents for better understanding
Related Articles

Mastering Real-World Cloud Run Services with FastAPI and Muslim
Discover how Google developer expert Muslim builds real-world Cloud Run services using FastAPI, uvicorn, and cloud build. Learn about processing football statistics, deployment methods, and the power of FastAPI for seamless API building on Cloud Run. Elevate your cloud computing game today!

The Agent Factory: Advanced AI Frameworks and Domain-Specific Agents
Explore advanced AI frameworks like Lang Graph and Crew AI on Google Cloud Tech's "The Agent Factory" podcast. Learn about domain-specific agents, coding assistants, and the latest updates in AI development. ADK v1 release brings enhanced features for Java developers.

Simplify AI Integration: Building Tech Support App with Large Language Model
Google Cloud Tech simplifies AI integration by treating it as an API. They demonstrate building a tech support app using a large language model in AI Studio, showcasing code deployment with Google Cloud and Firebase hosting. The app functions like a traditional web app, highlighting the ease of leveraging AI to enhance user experiences.

Nvidia's Small Language Models and AI Tools: Optimizing On-Device Applications
Explore Nvidia's small language models and AI tools for on-device applications. Learn about quantization, Nemo Guardrails, and TensorRT for optimized AI development. Exciting advancements await in the world of AI with Nvidia's latest hardware and open-source frameworks.