How to build AI agents: Beginner’s Guide

AI agents are the new revolution of the moment, which are having a significant impact on multiple sectors, transforming the way we work, interact, and make decisions.

Whether in automation and productivity, customer care and user experience, medicine, personalized learning, or digital content creation, they are completely transforming the business landscape. Building our own agent can be tricky at first, so we’ve compiled a series of recommendations and steps to follow to make it a simpler task. Let’s get started!

Basic concepts in AI agent creation

A few weeks ago, we shared with you the details about what AI agents are and how they work, which are systems or programs capable of autonomously performing tasks on behalf of a user or another system by designing their workflow and using available tools. They use the advanced natural language processing techniques of LLMs to understand and respond to user input step by step and determine when to call on external tools.

They use backend tool calls to obtain up-to-date information, optimize workflow, and autonomously create subtasks to achieve complex objectives.

In this process, the autonomous agent learns to adapt to user expectations over time. This ability to store past interactions in memory and plan future actions fosters a personalized experience and comprehensive responses.

Therefore, to develop and train an AI agent, we must teach it to understand and respond to human language in a useful and relevant way, as well as to have quality data and understand the key concepts around artificial intelligence, such as machine learning or natural language processing.

How to Build AI Agents for Beginners

As mentioned above, the basic component of agentic systems is an LLM enhanced with functions such as retrieval, tools, and memory. Thus, models can actively use these capabilities to generate their own search queries, select the appropriate tools, and determine what information to retain.

To this end, the best recommendation is to focus on two key aspects of implementation: tailoring these capabilities to each specific use case and ensuring that they provide a simple, well-documented interface. To do this, we recommend the following steps:

Define the purpose and scope

When creating an AI agent, the first thing to do is to clearly define what you want to do with it. By this, we mean deciding the specific tasks and functions it will perform.

• List the problems you want the agent to solve or the tasks it should handle: Will it be an autonomous agent? Does it need to answer customer questions? Will it help users buy online?
• Identify the target audience: Each user has different expectations and ways of interacting with technology, so you need to determine who you want to reach.
• Consider specific use cases or situations in which this AI agent will be used: This will help clarify what features and capabilities are needed.

Collect and prepare training data

An AI agent learns from data, so if the data is incorrect or of poor quality, it will learn and replicate incorrect things. Therefore, quality data ensures that the AI can accurately understand and process each user’s input.

To train this agent, data must be collected that reflects the type of interactions it will have with users, such as text transcripts, voice recordings, or interaction logs.

Once you have this data, you will need to clean it and prepare it for training. This involves removing irrelevant or incorrect data, correcting errors, and ensuring consistency throughout the data set. In addition, the last step would involve adding labels to describe what each piece of data represents.

Selecting the right technology and tools

In this step, the various available technologies must be evaluated, and those that best suit the purpose, data, and needs of the AI agent must be selected.

To do so, the following must be taken into account:

• Evaluate requirements: the specific tasks to be performed must be identified, as understanding language, recognizing images or making decisions require different technologies.
• Evaluate AI technologies: you will have to choose the ML framework that best suits the complexity of the project, as well as one library or another for PLN or Computer Vision tools that have to deal with visual tasks.
• Consider deployment options: Whether you want it to be on-premises or in the cloud or edge computing, it will be a very important decision if the applications require real-time processing or are scalable.
• Evaluate development tools: choose between integrated development environments or data management tools.

Designing the AI agent

In this step, the basis of how the agent will work and how it will perform its tasks must be established. To do this, 4 key points must be taken into account:

1. Agent architecture: Selecting the right architecture facilitates efficient operation and easy maintenance. We have two main options:
   1. Modular design: Individual AI agent components are developed separately and then integrated, which facilitates upgrades, debugging, and scalability.
   2. Concurrent design: multiple are executed simultaneously, making it the ideal choice for agents handling real-time operations or concurrent interactions.
2. Key functions: the main tasks to be performed by the agent should be listed, how users will interact with the agent, as well as incorporating feedback systems to help the agent improve over time.
3. Plan data flow: 3 parts should be taken into account here:
   1. Input handling: Determine how the agent will receive and process the data.
   2. Processing logic: Describe the steps the agent will follow to process data and generate results.
   3. Result generation: Specify the results that the AI agent will produce, such as predictions, classifications, or actions, and how they will be communicated to users or systems..
4. Establish decision-making processes: Here, it is necessary to select the algorithms that are aligned with the agent’s objectives and the complexity of the tasks, as well as the design of policies that define the rules that guide its behavior.

AI Agent Development

The penultimate stage is to develop the custom AI agent, which encompasses coding, integration, and testing to transform the conceptual design into a functional system.

The first phase of this step would be to code the core features, ensuring that they align with the design and functionality requirements from the previous step.

If a modular architecture has been chosen, the agent should be broken down into smaller, manageable components that can be developed, tested, and upgraded independently. Each module must fulfill a specific function, such as language processing and decision making, before being integrated into the complete system.

Once the main functions have been implemented, the focus should be on integrating the AI agent with the external systems required for its operation, such as making API calls and database integrations.

Next, to allow custom agents to learn and improve over time, memory and ML systems must be incorporated.

The next move has to do with testing and debugging, which play an important role in the process:

• Unit testing: test individual components to verify that each one works as intended.
• Integration testing: After unit testing, check how the different modules work together.
• Performance testing: Here, the agent is tested under various conditions to evaluate its response time, accuracy, and overall stability.

Implement and monitor the agent

Once the AI agent has been developed and tested, it is ready to be deployed in a real environment.

However, there is something to keep in mind before deploying it. It is best to create an environment that mirrors your production system to test the agent’s performance in a near real-world scenario. This will ensure that agent operations are not compromised under real-world conditions.

Once we get past this point, you can start using deployment strategies such as incremental upgrades, blue-green deployment, and rollouts to achieve a smooth transition without disrupting existing systems.

Another recommendation to keep in mind is to first launch the agent to a small, selected group of users to gather feedback for fine-tuning before rolling it out on a large scale.

Once implemented, it is time to continually review the agent’s performance, including response time, accuracy, and user satisfaction. This will help us identify areas of improvement and refine the agent’s functionality.

Best AI Agent Builder

Developing custom AI agents involves numerous challenges, such as data quality and quantity, algorithm selection and model complexity, real-time processing, scalability, regulatory and ethical compliance, and user interaction and experience, among others.

To make life easier for developers, there are a few tools that make this task more intuitive.

Vertex AI Agent Builder

Vertex AI is an artificial intelligence platform developed by Google Cloud that emerged as an evolution of Google’s previous AI services, such as AutoML and AI Platform, which aims to give developers and data scientists a more complete and easy-to-use solution.

It provides APIs for leading base models and tools to rapidly prototype, easily tune models with proprietary data, and seamlessly deploy them into applications.

It has a unified interface for developing, deploying, and managing AI models, as well as greater flexibility in the choice of frameworks and development tools, making it one of the most attractive platforms in the AI market.

LangGraph

LangGraph is a LangChain-based library that enables the creation of AI agents with advanced reasoning capabilities and custom workflow structures. It uses directed graphs to define agent logic, facilitating the creation of conversational systems with multiple steps and complex state handling. Its integration with LangChain allows leveraging language models (LLMs), external tools, and memory functions, making it ideal for dynamic and adaptive conversational flows.

To develop agents with LangGraph, a graph of nodes is defined, where each node represents an action, such as a call to an LLM, access to an API, or rule-based decision-making. This facilitates the construction of modular, reusable, and scalable AI systems. In addition, it is useful in scenarios where precise control over the flow of conversation is needed, such as virtual assistants, enterprise chatbots, or advanced technical support systems.

Beam AI

Beam AI is a platform designed to simplify the deployment of AI models in production. Its main focus is to provide an optimized infrastructure to deploy language models efficiently, with support for orchestration, scalability, and real-time monitoring. Beam AI facilitates the integration of models into applications through customizable APIs and secure execution environments.

Creating an agent with Beam AI involves defining its behavior and connecting it to external data sources or specific tools. The platform handles automatic scaling and performance maintenance, making it ideal for companies that want to deploy conversational agents without worrying about the underlying infrastructure. Its support for open source and commercial models allows it to adapt to a variety of needs, from virtual assistants to AI data analytics.

Microsoft Copilot Studio Agent Builder

Microsoft Copilot Studio is a low-code tool that allows you to create custom virtual assistants within the Microsoft ecosystem. It integrates advanced AI capabilities with Microsoft Copilot and Power Virtual Agents, making it easy to build conversational agents without deep programming skills. Its intuitive graphical interface allows you to define conversation flows, connect databases, and leverage integration with Microsoft tools such as Teams, SharePoint, and Dynamics 365.

To develop an agent with Copilot Studio, users can use pre-trained models, add custom rules, and connect the agent to various information sources. The platform offers customization options using Power Automate and Azure AI, making it suitable for companies looking to automate internal processes, customer service, or internal employee assistants.

AutoGen

AutoGen is a framework from Microsoft Research that enables the creation of autonomous AI agents with advanced reasoning and collaboration capabilities. Its approach is based on communication between multiple specialized agents, which can interact with each other and with the user to solve complex tasks efficiently. AutoGen facilitates coordination between language models, external services and knowledge bases, optimizing the decision-making process.

To build agents with AutoGen, developers can define individual agents with specific roles, such as data analysts, scripting assistants, or automatic schedulers. These agents can interact using auto-completion techniques, code generation, and dynamic workflow execution. Its modular and flexible design makes it ideal for applications such as task automation, content generation, and wizards specializing in technical domains.

The creation of AI agents requires careful planning and execution at all key stages, so the best option is to have a partner to help us in this task. At Plain Concepts, we specialize in helping our clients design their strategy, protect their environment, choose the best solutions, close technology and data gaps, and establish rigorous oversight to achieve accountable AI. You can achieve rapid productivity gains and build the foundation for new business models based on hyper-personalization or continuous access to relevant data and information.

We have a team of experts who have been successfully applying this technology in numerous projects, ensuring the security of customers. We have been bringing AI to our clients for more than 10 years, and now we propose a Framework for the adoption of generative AI:

• Unlock the potential of end-to-end generative AI.
• Accelerate your AI journey with our experts.
• Understand how your data should be structured and governed.
• Explore generative AI use cases that fit your goals.
• Create a tailored plan with realistic timelines and estimates.
• Build the patterns, processes, and teams you need.
• Deploy AI solutions to support your digital transformation.

¡Start now to creat your own AI Agent!