How to Build an AI Agent from Scratch?

What transforms a dedicated athlete into a gold medalist? Training. What elevates a musician to the status of virtuoso? Again, training. This principle of training extends beyond individuals to the realm of technology, particularly artificial intelligence (AI).

Today, businesses realize the crucial role of training AI agents to push their growth and innovation. Training these agents to understand human language can make organizations interact better and expand the capabilities of AI beyond what was previously possible.

In this blog, we delve into What are AI Agents, explore fundamental building AI Agents and training, and also break down step-by-step how one can create one's own AI agent from the ground up. Let's all join in the exciting journey as we delve further into the exciting world of AI development!

What is an AI agent?

An AI agent is a computer program designed to act autonomously in search of specific goals set by its user. Unlike other models, like ChatGPT, which heavily rely on human input, AI agents work more independently. Such autonomous systems can perceive the digital landscape, gather relevant data, use various tools, and make decisions to achieve their objectives.

The main difference between AI agents is that they are proactive and possess high-order reasoning capabilities. They continuously learn from their experiences, thereby becoming better over time. The process of reflection through interactions with data, their environment, and human feedback helps them adapt and fine-tune their performance, which in turn increases their effectiveness toward the attainment of their goals.

Types of AI agents

Some categories of AI agents exist, for example, that serve many different purposes at varying levels of complexity. Key ones include;

• Simple Reflex Agents: These refer to the very basics in AI terms. They recognize stimuli in a surrounding environment from predetermined rules as their response source without any knowledge of memory. An example could be an automatic responder for the e-mail sent, which generates some pre-recorded replies automatically.
• Model-Based Reflex Agents: Model-based agents are more like simple reflex agents, except for the fact that they have some internal representation of the world. That is, this model provides some context for actions and can generate more informed responses. Example: Smart home security systems that respond to changes in the environment.
• Goal-Based Agents: These agents operate with specific objectives in mind. They actively work towards their goals by planning and considering different approaches to achieve them. Example: Smart heating systems that optimize energy use based on user preferences.
• Learning Agents: These AI systems improve their performance over time by learning from experiences, feedback, and interactions with their environment. They can adapt their strategies to achieve better outcomes. Example: Customer service AI chatbots that refine their responses based on user interactions.
• Utility-Based Agents: Utility-based agents assess various options and select actions that maximize overall utility. They assign numerical values to different outcomes, allowing them to make decisions that balance competing goals. Example: Netflix’s recommendation engine, which suggests content based on user preferences and viewing history.

How Do AI Agents Work?

AI agents function through a structured process that involves several key steps:

• Perception and Data Collection: The AI agent starts by collecting data from a wide range of sources, such as customer interactions, transaction records, and social media. This information is essential for a deep understanding of the context and subtleties of customer inquiries. Advanced AI agents can input and process all this information in real time, and arm themselves with the latest information, ready to answer queries.
• Decision-Making: AI agents use complex deep learning models to analyze the collected data and recognize patterns to make informed decisions. For example, they can determine the best response to a customer query based on past interactions and the current context. Their ability to learn from previous experiences and refine their responses enhances the decision-making process.
• Execution of Action: Upon reaching a decision, AI agents will execute the action. For instance, an action could be a response to a customer inquiry, a request process, or an escalation of a complicated issue to a human agent. The process is supposed to be seamless and efficient in execution to ensure customers get prompt and accurate responses.
• Learning and Adaptation: AI agents continually learn from each interaction, refining their algorithms to boost accuracy and effectiveness. They update their knowledge base and utilize feedback to enhance future interactions. This ongoing learning capability ensures that AI agents remain effective and relevant, even as customer expectations and business environments evolve.

AI Agent Development Frameworks

There are two general ways to develop an AI agent: either building from scratch or using existing orchestration frameworks. The choice depends on the constraints and priorities of your business, such as budget, timeline, and level of customization. 

Here's an overview of each option so you can make an informed decision:

1. Leveraging Existing Orchestration Frameworks

Existing AI agent frameworks provide pre-built components for common functionalities, streamlining the development process. These frameworks typically leverage Large Language Models (LLMs) as their core, offering libraries and tools that simplify the creation, deployment, and management of AI agents. Some popular frameworks include:

LangChain: An open-source framework for building LLM-powered agents that can interact with external data sources and APIs, ideal for sophisticated applications.

LlamaIndex: This framework enables agents to retrieve and process data from diverse sources by integrating LLMs, enhancing the agent’s capabilities.

AutoGen: Focused on multi-agent systems, AutoGen facilitates conversational AI and task automation through agent collaboration.

Phidata: A Python-based framework that transforms LLMs into agents, providing a built-in UI for managing and deploying them.

Pros

• Faster development time due to pre-built components.
• Requires less technical expertise.
• More cost-effective compared to Building AI Agents from scratch.

Cons

• It is not flexible and dependent on third-party frameworks.
• Possibly restricted access to certain data sources.

Best For

Organizations with less AI experience, time constraints, or fewer resources for investment. This option is suitable for applications that do not require high customization or complexity and allows for the exploration of AI capabilities without significant upfront costs.

2. Build an AI Agent System from Scratch

The ground-up construction of an AI agent allows you the maximum amount of control over its design and functionality. This is most suited for organizations looking to develop agents for niche-specific tasks or highly complex use cases.

Developing from scratch, however, requires a good amount of knowledge in machine learning and software engineering and a huge amount of time and resources.

Pros

• Custom-tailored solutions that align perfectly with your business needs.
• Full ownership of the AI system and its underlying model.
• Access to a wider range of data sources, enhancing the agent's capabilities.

Cons

• High technical expertise requirements can be a barrier.
• Long and costly development cycles can strain resources.
• Ongoing maintenance and optimization of a custom AI system can be challenging.

Best For

Enterprises having a significant amount of budget available for development and access to great resources and for unique use cases that require deep customization. The approach is meant for businesses that can invest significantly in extensive AI development and have a high demand for customization.

How to Build an AI Agent from Scratch

How to Build AI Agents for Beginners involves a systematic approach to ensure that the final product effectively meets the desired objectives. Here’s a step-by-step guide:

1. Define the Purpose and Scope

• Identify Objectives: Clearly outline what you want the AI agent to achieve. Consider specific tasks it will perform, such as customer support, data analysis, or process automation.
• Identify Use Cases: Identify scenarios in which the AI agent will work. Knowing this will help make its features and functionalities more apt.

2. Form a Development Team

• Get the Experts: An ideal team comprises skills such as that of machine learning engineers, software developers, data scientists, and UX/UI designers. The team needs to be equipped with a clear idea of AI technologies and software development.
• Assign Roles: Define roles and responsibilities within the team clearly to help streamline the development process and provide accountability.

3. Collect and Prepare Data

• Data Collection: Gather all relevant data to the AI agent's task at hand. This could be in the form of historical data, user interactions, or public datasets.
• Data Preprocessing: Clean, organize, and transform the data to ensure it is suitable for training the AI models. This step may include handling missing values, normalizing data, and feature extraction.

4. Choose the Right AI Framework

• Evaluate Frameworks: Research and select appropriate AI frameworks and libraries that fit your project requirements. Consider factors such as ease of use, community support, and compatibility with your technology stack.
• Popular Options: Some commonly used frameworks include TensorFlow, PyTorch, and Keras for deep learning applications, depending on your specific needs.

5. Design the AI Agent Architecture

• Architecture Planning: Develop a high-level architecture that outlines the various components of the AI agent, including data processing modules, machine learning models, and communication interfaces.
• Modular Design: Aim for a modular design that allows for easy updates and maintenance. This can improve flexibility and scalability as the agent evolves.

6. Train the AI Agent

• Model Selection: Choose appropriate machine learning models based on the tasks the agent will perform. For instance, consider supervised learning for classification tasks or reinforcement learning for decision-making processes.
• Training Process: Train the model using the prepared dataset, fine-tuning hyperparameters to optimize performance. This step may require iterative testing and adjustments to achieve the desired accuracy.

7. Test the AI Agent

• Testing Strategies: Implement various testing strategies, including unit tests, integration tests, and user acceptance testing (UAT), to ensure the AI agent operates as expected.
• Performance Evaluation: Measure the performance of the AI agent using relevant metrics, such as accuracy, precision, recall, and response time, to identify areas for improvement.

8. Deploy and Monitor

• Deployment Strategy: Deploy the AI agent in a suitable environment, whether on-premises or in the cloud. Ensure that it is accessible to users and integrates seamlessly with existing systems.
• Monitoring and Maintenance: Continuously monitor the AI agent’s performance and user interactions. Collect feedback to make necessary adjustments and updates, ensuring the agent remains effective and relevant over time.

AI Agent Use Cases

1. Customer Service

AI agents can respond to consumer questions via chatbots or virtual assistants, offering prompt answers to commonly asked questions, resolving problems, and assisting users with procedures. This lessens the workload for human agents while improving customer satisfaction.

2. Customized Suggestions

AI agents are used by e-commerce platforms to examine user preferences and behavior to provide tailored product recommendations. This has the potential to greatly boost sales and enhance customers' overall shopping experiences.

3. Task Automation

AI agents can automate routine tasks like email management, appointment scheduling, and data entry. Employees can concentrate on higher-value work as a result of the streamlined operations.

4. Lead Generation and Sales

AI agents qualify leads by chatting or emailing the prospect, collecting information, and scoring leads based on the probability of conversion. This may improve the productivity of the sales team.

5. Data Analysis and Insights

AI agents are capable of processing large datasets. They can help in identifying the patterns and trends that humans would not be able to identify. This can lead to actionable insights for businesses that will help businesses in decision-making and strategy building.

6. Healthcare Assistance

AI agents can help with patient scheduling, symptom checking, and medication information. The agents can analyze patient data, supporting medical professionals in diagnosis and treatment planning.

7. Travel and Booking Services

AI agents can find and book accommodations, flights, and activities. They can be used to make real-time travel itinerary updates as well as modify or cancel any bookings.

8. Content Creation and Curation

AI agents can write content for blogs, social media, and marketing campaigns. They can curate relevant content from various sources, saving content creators and marketers time.

9. Fraud Detection

In financial services, AI agents can monitor transactions for suspicious activity and flag potential fraud. They use machine learning algorithms to analyze patterns and identify anomalies.

10. Smart Home Management

AI agents can control smart home devices, managing everything from lighting and temperature to security systems. They can learn user preferences and automate routines for convenience and energy efficiency.

Conclusion

AI agents have the power to redefine operational efficiency and transform entire sectors. Through work automation, resource optimization, and data-driven decision-making, they provide substantial time and cost savings. We can anticipate the emergence of increasingly complex AI agents as technology develops further, completely altering how companies function.

Are you prepared to use AI agents to your company's advantage? At Prioxis, we create and deploy unique AI solutions based on the requirements of our customers. Let's construct one for you as well. Get in touch and speak with our knowledgeable staff right now.