This blog, co-authored by Ali Arsanjani from Google Cloud, highlights the rise of AI agents and their transformative role across various industries. As organizations shift from testing to real-world applications, they face challenges such as complexity, deployment issues, and the need for monitoring agent performance. The combination of Google’s Agent Developer Kit and Arize’s observability tools offers a comprehensive solution to these problems. This partnership helps in building, deploying, and continuously improving AI agents while ensuring they function effectively in business-critical applications. The tutorial provides a detailed guide on setting up the environment, developing, and deploying an AI agent on Google’s platform, aiming to simplify the development process.
Co-authored by Ali Arsanjani, Director of Applied AI Engineering at Google Cloud
1. Introduction: The Dawn of the Agentic Era
We are stepping into a significant new phase of AI technology, marked by exciting trends such as Multimodality, Multi-agent Systems, and Agentic Workflows. These advancements are revolutionizing how businesses utilize AI agents across various sectors. These agents are now capable of handling complex tasks, significantly improving efficiency in areas like customer service, data analysis, content creation, and process automation.
However, as companies transition from initial experiments to actual deployment, they encounter several challenges:
– Complexity: Crafting sophisticated AI agents involves managing numerous components, including large language models (LLMs) and intricate workflows.
– Deployment: Moving agents to production presents issues regarding infrastructure, scalability, and reliability.
– Observability: The opaque nature of LLMs complicates understanding agent behavior, making problem diagnosis difficult.
– Performance: Without adequate evaluation methods, pinpointing optimization opportunities and measuring progress becomes tricky.
To address these hurdles, Google’s Agent Developer Kit combined with Arize offers a robust solution. Google’s framework is a flexible, production-ready base for building and deploying agents, irrespective of the orchestration framework used. Arize enhances this with specialized tools for observability tailored for modern AI systems, giving a clear view into agent behavior and performance, crucial for diagnosing problems.
In this article, we will delve into how these powerful platforms collaborate to create reliable and observable AI agents.
2. Google Agent Developer Kit and Arize: A Powerful Combination
As AI agents evolve from experimental tasks to critical business tools, organizations require solutions that cater to both development and operational needs. The synergy between Google’s Agent Developer Kit and Arize offers a comprehensive platform covering the entire agent lifecycle, from development to continuous improvement.
Google Agent: Build Once, Deploy Anywhere
The Google Agent Developer Kit provides a versatile setting for developing and executing agents. It is framework-agnostic, supporting various platforms like LangGraph and LangChain, which allows teams to design specialized agents that work in unison. It features a managed runtime via the Vertex AI Agent Engine, facilitating deployment, scaling, and operations, alongside seamless integration with other tools and enterprise systems.
Arize: Unified AI Observability and Evaluation Platform
Arize accelerates the development and optimization of AI applications and agents. With features like OpenTelemetry Tracing, automated evaluation assessments, and prompt design tools, it ensures extensive insights into agent function and guides continuous refinement.
Together, Google’s and Arize’s platforms effectively cover the agent lifecycle, offering comprehensive support from development through deployment to ongoing improvement.
3. Tutorial: Setting Up Your Environment
To build and deploy an observable AI agent, you need to configure your development environment appropriately. Begin by creating a Google Cloud project with Vertex AI enabled and signing up for an Arize AX account. You will also need to install specific Python packages necessary for running the AI agent.
4. Building and Testing a Simple Agent
Now, let’s build a basic agent using LangGraph, designed to provide product information. Start by setting up a function that retrieves product details and implement a routing logic controlling how the agent interacts based on user queries.
Once your agent’s capabilities are defined, you can test it locally to ensure it accurately responds to different queries. This phase allows you to investigate how the agent handles requests that may fall outside its designed functions.
5. Deploying the Agent to Vertex AI Agent Engine
After confirming that your agent functions well locally, it’s time to deploy it to the Vertex AI Agent Engine. This managed service simplifies the deployment process, allowing you to concentrate on refining the agent’s abilities. Use the Vertex AI SDK to package your agent and its required dependencies into a deployable format.
6. Testing the Deployed Agent
Once deployed, you can interact with the agent using the same methods as before. Ensure that it responds accurately to queries as it did during local testing, confirming it is operational on Google’s infrastructure.
7. Viewing Traces in Arize AX
With the Arize setup, trace data from your agent’s interactions flows directly to your Arize account. By logging into Arize and exploring the Tracing tab, you will gain valuable insights into how your agent behaves and where improvements may be beneficial. The dashboard allows for monitoring interactions, evaluating performance, and identifying potential enhancements.
8. Conclusion and Final Thoughts
In this blog, we examined how Google’s Agent Framework and Arize work together to create a formidable solution for building, deploying, and refining AI agents. This partnership enables organizations to transition from experimental AI projects to full-scale, production-ready systems consistently affiliated with addressing security, observability, and continuous improvement needs.
By embracing this integrated approach, businesses can leverage AI agents to offer substantial value to users while adapting to the evolving technological landscape.
Further Resources
Google Cloud Resources
Arize Resources
Tags: AI Innovation, Google Cloud, Arize, AI Agents, Observability, Technology Advancements
What are Observable AI Agents?
Observable AI Agents are smart tools that can learn and make decisions based on data. They help in automating tasks and improving processes in various applications.
How do I start building an AI agent with Google Agent Framework?
To start building an AI agent, you first need to set up your environment with the Google Agent Framework. You will create a project, define the tasks for the AI, and then write code to teach it how to perform those tasks.
What role does Arize play in AI deployment?
Arize is a platform that helps monitor and analyze AI models after deployment. It ensures your AI agents are performing well and helps identify any issues so you can fix them easily.
Can I customize my AI agent?
Yes, you can customize your AI agent to fit your specific needs. You can adjust how it learns, the types of data it uses, and the tasks it performs to better suit your business or project requirements.
Do I need coding skills to build an AI agent?
Having some coding skills is helpful when building an AI agent, especially for writing custom functions. However, you can find user-friendly interfaces and tools that make it easier for beginners to get started.