Conversational artificial intelligence and Agentic applications are revolutionizing the way we interact with technology. These applications, powered by Large Language Models (LLMs), are capable of understanding context, reasoning, planning, and executing actions autonomously to achieve complex objectives. Vertex AI is Google Cloud's end-to-end Machine Learning platform that offers a robust set of tools and services to facilitate the creation of these next-generation Agentic applications.
Below, we will detail the 10 steps necessary for you to build an Agentic Application with the help of Vertex AI:
- Define the Use Case and Objectives: Regardless of the technology you are going to use, this is the first fundamental step in the development of any application, including Agentic applications. Clearly identify the problem your application will solve and the objectives it should achieve. In addition to quantifying the benefits of the application, pay attention to the estimated costs. In GenAI solutions, these costs can be more complex to understand than in a traditional application.
- Select the Language Model: Choose the LLM that best suits your needs in terms of language comprehension, reasoning, and text generation. Consider the size of the model, its performance, and, once again, its cost. Google Cloud includes Model Gardenin Vertex AI, a centralized place to discover and test a variety of models, including foundational models, task-specific solutions, and open-source models. Among others, we can find the latest versions of Gemini, Gemma, Claude (Anthropic), and Llama.
- Design the Prompts: The "Art" of generating prompts is becoming increasingly important and in demand in the market. An Agentic application needs effective prompts that guide the LLM to understand user intentions, reason about the problem, and generate relevant responses or action plans. Vertex AI Studio is the Google Cloud tool that will allow you to experiment with different prompt structures, adjust parameters, and evaluate the quality of responses to optimize the agent's behavior.
- Implement a Retrieval Augmented Generation (RAG) System (Optional): A RAG system allows, in real-time, to provide the prompt that the LLM will consume with additional context information extracted from your data sources. This provides key benefits such as the reduction of model hallucinations (instances where the model generates content that isn't factual), the anchoring of model responses to your data sources, and the enhancement of the trustworthiness and applicability of the generated content by providing citations and confidence scores. In this case, Google Cloud provides Vertex AI RAG engine and Grounding from Google Search and from Vertex AI Search with the aim of complementing our prompts with context information and improving the quality of the responses. As always, it is necessary to evaluate if our Agentic application needs a RAG system since this can increase the cost and latency of our application.
- Fine-Tuning Your Models (Optional): Unlike prompting, fine-tuning takes the customization of AI models a step further. While prompting focuses on designing precise instructions to guide pre-trained models, fine-tuning adapts the model to your specific domain, retraining it with your own labeled data. This technique allows creating AI models that excel in complex or unique tasks, especially when a significant dataset is available for training. Parameter-Efficient Fine Tuning, or PEFT, is the most used strategy in these cases because it is faster and cheaper than Full Fine Tuning, at the expense of response quality. Vertex AI allows supervised fine-tuning on foundational models.
- Implement Agent Logic: This implementation, which may include managing the conversation flow, planning actions, interacting with external APIs, and handling errors, can be done directly using the LLM APIs and "classic" implementation techniques in the cloud. This offers great flexibility and control over the developed agent, albeit at the cost of greater complexity in the implementation and subsequent maintenance. However, if you do not require so much flexibility, an easier option is to use low-code agent building tools. In Google Cloud, this functionality is provided by the service Vertex AI Agent Builder. This tool offers a visual and low-code environment to define complex conversation flows, manage interaction status, configure business logic, and connect your agent to various tools and external APIs.
- Integrate External Tools (Optional): If your Agentic application needs to interact with the outside world, integrate the necessary APIs and tools. For example, a travel agent could integrate with flight and hotel booking APIs.
- Test and Evaluate: Thoroughly test your Agentic application with different scenarios and users to evaluate its performance, identify areas for improvement, and refine the prompts and logic. For this, it is very useful to have evaluation tools that allow you to benchmark against your own evaluation criteria. Google Cloud provides GenAI Evaluation Service for these tasks.
- Deploy the Application: It is time to deploy your application on a platform that allows the execution and scaling of your Agentic agents so that it is accessible to your users. Here, you should always look at the deployment, scalability, security, elasticity, and ease of maintenance facilities of the platform we are going to use. Vertex AI Agent Engine is the Google Cloud platform that provides the infrastructure and capabilities necessary to deploy, manage, and optimize the performance of agents in production.
- Monitor and Improve: This is also applicable to any application and not only to Agentic applications. Always monitor the performance of your applications in production and collect user feedback to identify opportunities for continuous improvement.
The era of Agentic applications has begun, and the organizations that know how to harness their potential will be the ones that lead the next wave of digital transformation. At dataguru, we believe that collaboration is essential to build the future, especially in the field of Agentic applications. If you have an innovative idea and need a team with the experience to develop it, we invite you to contact us. Let's explore together how we can create incredible solutions with Agentic AI.
