How to Build an AI Agent

What makes an athlete a gold medalist? Training. What makes a musician a virtuoso? Training. But training doesn't just apply to people. Now, businesses are seeing the value of training artificial intelligence (AI) to help them move forward. Building and training an AI agent is becoming essential for growth, and by teaching an AI agent to understand human language, it can respond better, and perform more useful tasks than ever before.

As AI technology advances, these agents will become more sophisticated and capable, bridging the gap between human expectations and AI performance. So, let’s find out what an AI agent is all about, the basics of building and training AI, and the steps to train one on your own.

What we'll cover

• What is an AI agent?
• Understanding the basics of building AI agents
• The 6 steps of building and training AI agents
• Build and train your own AI agent

An AI agent is a computer program designed to help people by performing tasks and answering questions. The key term here is helping people.

Artificial intelligence (AI) agents help with everyday tasks, like managing emails and scheduling appointments, by learning from a variety of language inputs. These tasks can range from setting reminders and managing schedules to providing information like weather updates or news. AI agents are programmed to understand and respond to human language, making interactions with them more natural and user-friendly.

There are many types of AI agents, including as assistive agents and autonomous agents. An example of assistive agents are those that can be embedded within employee tools to help them with personalized tasks that are specific to their role. Meanwhile, autonomous agents can understand and respond to customer inquiries without human intervention. This is done by using an agent builder, like Agentforce, to create agent operate dynamically — as opposed to following predefined rules — and are triggered by changes in data and automations

Training an AI agent involves several key steps to ensure it works effectively and efficiently. This includes data collection and preparation, model training, evaluation, fine-tuning, and deployment. It also includes monitoring and updating your agent to make sure it stays in line with your goals. Let’s dig into the steps so you can learn to do this yourself.

Building and training an AI agent involves teaching it to understand and respond to human language in a way that’s useful and relevant. From generative AI (GenAI) to conversational AI, your data is at the heart of it all. Training incorporates several key concepts from the fields of artificial intelligence, particularly machine learning and natural language processing (NLP). Let’s review each.

Machine learning (ML) is a type of AI that provides systems the ability to automatically learn and improve from experience without being programmed. When training an AI agent, machine learning algorithms use historical data (examples of human interactions) to find patterns and make decisions. The more data the AI processes, the better it gets at predicting and responding to user requests.

Natural language processing (NLP) is a branch of AI that deals with the interaction between computers and humans through natural language. The aim is for computers to process and understand large amounts of natural language data. In the context of an AI agent, NLP enables the system to understand, interpret, and generate human language in a way that is both natural and meaningful.

Data labeling is a key step in training AI where humans annotate data — adding meaningful tags or labels to the raw data so that the AI can understand it. For example, in training an AI agent, data labeling might involve tagging parts of speech in sentences, identifying the sentiment of a text, or categorizing queries into topics. This labeled data then serves as a guide for the AI to learn from and uses these labels to understand the context and intent behind user inputs.

When building an AI agent, the first step is to clearly define what you want it to do. This involves deciding on the specific tasks and functions the agent will perform. Here’s how to approach this:

First, determine the tasks and functions of the AI agent. List the problems you want the AI agent to solve or the tasks you want it to handle. Do you want an autonomous agent? Do you need it to answer customer queries, help users shop online, or provide information about your business? The functions of your AI agent should align with the needs it aims to fulfill.

For instance, do you need a virtual shopping agent? This agent helps users navigate online stores, offering personalized shopping advice based on user preferences and past shopping behavior. It can suggest gift ideas, find the best deals, or even help with fashion choices.

Next, identify your target audience. Different users have different expectations and ways of interacting with technology. For example, an AI agent designed for medical professionals might need to understand and use medical terminology accurately.

And, consider use cases or specific situations in which your AI agent will be used. Defining these can help clarify what features and capabilities are necessary. For instance, a customer service chatbot needs to handle inquiries, complaints, and possibly transactions, while a virtual shopping agent should be able to suggest products, compare prices, and understand user preferences.

Just like a student learns from textbooks, an AI agent learns from data. If the data is incorrect or of poor quality, the AI will learn the wrong things and make mistakes. High-quality data ensures the AI can accurately understand and process user inputs.

To train your AI agent, you need to gather data that reflects the kind of interactions it will have with users. This could include:

• Text transcripts: Collect transcripts of conversations from chat logs, support tickets, or emails that are similar to the expected interactions with the AI.
• Voice recordings: If the AI will respond to spoken commands or inquiries, voice recordings are essential to help it understand different accents, intonations, and speech patterns.
• Interaction logs: Data from previous interactions with similar systems can provide insights into user behaviors and common queries or commands.

Once you have your data, it needs to be prepared for training by cleaning it. This involves removing irrelevant or incorrect data, correcting errors, and ensuring consistency across the data set. For example, fixing typos in text transcripts or filtering out background noise in voice recordings.

And lastly, labeling it. This is about adding labels — tags or metadata — to describe what each piece of data represents. For instance, labeling a piece of text with the intent of the user, such as "booking a flight" or "asking for store hours." This helps the AI understand the context and purpose of user inputs.

This step is all about selecting the right machine-learning model which will determine how well your AI can learn from data and perform its tasks.

There are two types of machine learning models:

1. Neural networks: These are powerful models that mimic the way human brains operate. They are particularly good at processing large amounts of data and recognizing patterns, making them ideal for understanding and generating human language.
2. Reinforcement learning: This type of model learns through trial and error, using feedback from its actions to improve over time. It's useful for AI agents that need to make decisions or optimize their behavior based on user interactions.

So how do you choose the appropriate model?

Consider the AI agent’s functions and tasks you want it to perform. For example, if the agent needs to understand and generate human-like responses, a neural network might be the best choice.

And, consider the data you collected. Neural networks, for example, require large amounts of data to train effectively, while reinforcement learning might be suitable for scenarios where the AI can learn from ongoing interactions with users.

You also have the option of pre-trained models. These are models developed and trained by researchers on large datasets. They can be a great starting point because they have already learned a lot of general information about language and human interactions.

Here are some examples of pre-trained models:

• GPT (Generative Pre-trained Transformer): This model is excellent for generating text and can be fine-tuned to perform specific tasks like answering questions or writing content.
• BERT (Bidirectional Encoder Representations from Transformers): Known for its ability to understand the context of a word based on its surroundings, making it useful for tasks that require a deep understanding of language, such as sentiment analysis or language translation.

While pre-trained models are broadly knowledgeable, they might not be specialized in the specific tasks your AI agent needs to perform. You’ll have to fine-tune them. Fine-tuning involves continuing the training of a pre-trained model on your specific dataset, allowing it to adapt to the nuances of your particular application.

It’s time to actually train the machine learning model using the data you've prepared. This step is where your AI begins to learn from the examples you've provided, so it can eventually perform tasks on its own.

Here are the steps to train your AI agent:

1. Set up your environment: Before you start training, set up your machine learning environment. This could involve installing software libraries and frameworks that are necessary for machine learning.
2. Load your data: Import the cleaned and labeled data into your environment so it can be used for training.
3. Split the data: Divide your data into at least two sets: training and testing. The training set is what you'll use to teach your model, and the testing set is used to evaluate how well your model has learned.
4. Choose a model: Based on this decision, initialize the machine learning model that you want to train.
5. Configure training parameters: Set the parameters that will guide the training process. This includes the learning rate, batch size, and number of epochs. The learning rate dictates how much the model adjusts its parameters in response to the observed errors during data processing. The batch size is the number of data samples seen by the model before it updates its internal parameters. And, the number of epochs, which represents complete passes through the entire training dataset, affects learning depth. Most epochs provide the model with more opportunities to learn from the data.
6. Train the model: Start the training process. The model will use the training data to learn, adjusting its internal parameters to minimize errors.
7. Monitor the training process: Keep track of performance metrics such as accuracy or loss during training. These metrics will tell you how well the model is learning. If the model isn’t performing as expected, you might need to adjust the training parameters. For example, if the training loss is not decreasing, consider lowering the learning rate.

Developing an AI agent involves testing and validating the system to ensure it performs as expected and meets the goals you've set. This step helps you identify and fix any issues before the AI agent is fully deployed.

Start by running the AI agent through a series of predefined tasks or queries to see how it responds. This is like giving it a mini-exam to see if it learned what it was supposed to.

Measure how accurately and efficiently the AI agent performs tasks. Check if the responses are correct, how long it takes to respond, and whether the interactions are smooth.

Then, you’ll want to choose from the different testing methods:

• Unit testing: Test individual components or parts of the AI agent to ensure each one functions correctly on its own.
• User testing: Invite real users to test the AI agent in controlled settings. This helps you see how the agent performs in real-world scenarios and how users interact with it.
• A/B testing: Compare two versions of the AI agent against each other to determine which one performs better. For instance, you might test two different response styles or interaction flows to see which is more effective.

Be aware of overfitting and underperformance. Overfitting occurs when an AI agent performs well on the training data but poorly on new, unseen data. To address overfitting, you can use techniques like cross-validation, where you rotate the data used for training and testing to ensure the model generalizes well.

And, if the AI agent isn't performing up to expectations, consider revisiting the training phase to adjust parameters, add more data, or even retrain the model.

Set up mechanisms to collect feedback from users, such as surveys, feedback forms, or direct interviews. Pay attention to what users like and dislike, and what they find confusing. Use the feedback to make continuous improvements to the AI agent. This might involve tweaking the conversation flows, training the model with more data, or adjusting the user interface.

Finally, it’s time to deploy your AI agent in a live environment and find out how the AI interacts with actual users.

Decide where you want to deploy the AI agent — your website, within a mobile app, or on a voice-activated platform. Then, integrate the AI agent into your chosen platform. This might involve embedding code into a website, configuring the agent in a mobile app, or setting up the agent with the APIs of a voice platform.

Once integrated, launch the AI agent to start interacting with users. Ensure that all support systems are in place for a smooth launch.

Regularly check how well the AI agent is performing. Does it understand user queries correctly? How is it handling complex conversations? You can use tools that provide real-time insights into how the AI agent is performing. These tools can show you response times, success rates, and user satisfaction levels.

You can do this by collecting user feedback directly through the platform. This can be in the form of ratings, comments, or direct survey links after interactions with the AI agent. You can also set up error logging to capture when things go wrong. Get notified if there’s a sudden spike in errors or a drop in performance, allowing for quick action.

By deploying the AI agent carefully and setting up monitoring systems, you can ensure that it not only starts strong but also adapts and improves over time, continuing to meet user needs and expectations.

That completes your training! Building and training your own AI agent may seem like a lot, but with Salesforce you’re at the forefront of technological innovation that fuels your business forward. Gain access to cutting-edge tools and frameworks that simplify the training process, ensuring your AI agent is both intelligent and efficient. As you harness the power of AI, you'll reach new levels of productivity and insight, transforming data into actionable strategies for growth.

Welcome the future today by charting your agent journey with Salesforce, and set the stage for a smarter, more connected business environment.

Caylin White is an Editorial Lead and Growth Manager for Small Business at Salesforce. She has written content for over 15 years for many SaaS industries, like WordPress and BuzzSumo. She specializes in SEO but is sure to add a human-centric angle to every piece.