A Deep Dive Into AI Agents and How to Build AI Agents For Business Use Cases

In 2024, everyone who’s anyone knows about the power of AI and how it is revolutionizing businesses across industries. Today, businesses are transforming their customer experience through AI agent development and that’s what we are going to dive deeper into with this blog. AI chatbots are a great way to enhance your customer experience by giving customers access to 24/7 support through chat. While chatbots have been prevalent in the past they could only work for simple question answering and could not respond by understanding the contact of what a customer is asking for.

However, in today’s day and age AI has become so advanced and so accessible that businesses can easily build and integrate AI chatbots into their customer service process and AI agent development has also become accessible through open source AI models and integrations like ChatGPT.

Let’s dive a bit deeper into the topic and see how you can build AI gents for your business.

Introduction to How to Build AI Agents For Enhancing Your Customer Experience

Right off the bat, there are a few ways you can build AI agents from training open source Large Language Models (LLMs) to integrating ChatGPT APIs and we’re going to cover them all in this blog. This blog is not just going to be an AI agents tutorial bot but an insight into why you should build AI agents and what the nuances are when it comes to its business applications. AI agents are a great tool for your customer experience because customers don’t have to wait for human agents to attend to their grievances and questions.

Now, one thing that might cross your mind is do you really need to know how to build AI agents when there are SaaS products out there that also have AI integration? Realistically, if you have a service based business and if your services are broad then you can easily use one of the existing solutions for managing your customer service process. Here you don’t need much flexibility with your answers because you can program the third party with more or less generic responses. But if you specialize in a niche or if you are providing products for your customers then the questions your customers may have will also be diverse.

AI agent development is the right bet here because you can program the bot specifically for your business and even create multi-agent systems through custom software development. This allows you to take control of the customer experience and curate it according to the demands and changing preferences of your customers. Normally, you would have to create SOPs or Standard Operating Procedures for your customer service agents and make sure that they follow through. However, with AI agents you can prepare SOPs once or train the AI model on a document that outlines your product and procedures and the AI can then learn from them and generate responses for your customers accordingly.

Let’s Take A Look At What AI Agents Are And How They Work

AI agent development is the process of creating smart chatbots that use artificial intelligence to handle human-like conversations with your customers. While you can create chatbots that are not intelligent by programming them to answer questions based on predefined questions and answers, these are not applicable in every situation because customer queries will be different. On the other hand, AI agents are intelligent agent software that can understand the context of your customers’ queries and provide meaningful responses that address the customers’ actual needs. This requires a deep understanding of artificial intelligence and the customer service process and in later parts of our AI agent tutorial we’re going to cover the steps that you need to take to get started.

Discover The Core Technologies Behind AI Agents

In this blog, we are talking about how to build AI agents for your business and for this you have majorly 2 paths that you can take.

1. Training Open-Source AI Models
2. Integrating OpenAI APIs

We’re going to dive deeper into these concepts in just a moment but now let's look at the core technologies that empower AI agents.

Firstly, building an AI agent is not just about creating a chatbot that can answer your customers’ questions. In order for the chatbot to simulate human-like conversations it has to process language, make decisions and sometimes perform complex tasks without any human input. For this, in AI agent development we have to combine 3 technologies; Natural Language Processing or NLP, Machine Learning or ML and Deep Learning or DL.

Natural Language Processing or NLP

In reality, you can say that NLP is the foundation of your AI agent’s ability to understand, interpret and generate language that mimics human conversations. Although this seems like a singular task it actually involves the AI agent being able to perform tasks like text analysis, sentiment detection, language translation and question answering. The process involves a lot of complex jargon such as tokenization, part of speech tagging, named entity recognition and parsing through AI agent development. What it really means is that the NLP system breaks down your conversation or language into meaningful elements that the AI can process.

Modern NLP systems that we use today rely on transformer based models like BERT and GPT to help the AI understand the context of a human’s text and the relationships between words we use. For example, if a user says “my bottle came with a crack on the side” it is pretty straightforward and the AI can easily interpret that into ‘this customer needs a product replacement’ which is not as complex in terms of AI agent development. However, if the customer says “my bottle looks like someone played catch with it” the customer is actually saying that their product is defective and not that someone was playing catch before delivering the product.

The AI model has to understand the context of a customer’s text which is why modern AI agents use advanced NLP to generate meaningful responses.

Machine Learning or ML

Now, Machine Learning is exactly what it sounds like. It is a technology that allows AI agents to improve their performance over time by learning from patterns in data through AI agent development. This means that if you have a diverse set of data about your business you can use this data to train your AI model and once the training is done the AI model will be able to generate responses by having a much better understanding of your business and how it operates. You can use these AI models for a diverse range of tasks and functionalities from understanding language to personalization for customers.

For customer support this is a great feature because you can use existing data about customers and their needs to train the AI model and the chatbot will be able to handle more and more personalized conversations as your business progresses. In AI agent development engineers call this supervised learning and it is one of the main approaches that you can use to train AI agents to perform specific tasks based on labeled data. There’s also another technique called unsupervised learning where your AI agent does not rely on labeled data and can learn from the customers’ behavioral patterns. This kind of a system is frequently used in features like personalized recommendations.

Deep Learning or DL

Deep Learning is a subset of machine learning and allows you to give your AI agents much more advanced capabilities. DL can do this by creating layers of neural networks through AI agent development and these neural networks are capable of processing large amounts of data. Deep learning is used more for processing unstructured data like audio, images and text and this is the technology used for many popular apps that we use today like MidJourney, Stable Diffusion, and so on.

We use deep learning in NLP for applications that need these advanced capabilities and take the form of Recurrent Neural Networks or RNNs and transformers. You may have already figured out why deep learning is important when it comes to AI agent development but to specify RNNs are great for processing sequential data. This means that if you have data in a sequence like a customer’s conversation with the chatbot for example deep learning allows the chatbot to understand the conversation by understanding the entire conversation from top to bottom and generate more personalized and tailored responses to customers.

Over time, the chatbot will be able to understand the flow and structure of a sentence which will make the conversations more fluid and human-like. You can already see deep learning in action with tools like ChatGPT where the chatbot can understand and memorize your conversation when you’re writing prompts. Deep learning is crucial in AI agent development if you want your chatbots to be truly an AI agent and not just a robot that generates responses.

How Natural Language Processing, Machine Learning, and Deep Learning Work Together In AI Agents

We use NLP, ML and DL together in modern AI agent solutions because the synergy of these technologies allows us to build products that can simulate human-like conversations better than ever before. NLP is what helps your AI agent understand and interpret natural language which the customer types into the chat box. Machine learning is the next part of AI agent development and it allows your AI agents to learn from customer interactions to improve their responses over time. Deep learning is the final piece of the puzzle and it provides the AI agents with computational depth that you need to recognize complex patterns and generate accurate responses. A combination of these innovative technologies allows us to create self-learning AI agents that are capable of handling natural conversations with customers, use intelligent dialogue and recognize your customers’ needs to provide relevant results in real time. This is the crux of the AI agent development process.

Let’s Outline The Steps to Build AI Agents

As we spoke about in earlier parts of the blog we are going to look at 2 methods that you can take to build your AI agent. Number one is to create an AI agent using open source AI models. There are many models out there and in fact even OpenAI’s earlier models are available open source so you can take the model and train it with your own data to create a very capable chatbot. There is a second method for AI agent development where you can use OpenAI’s APIs to create the chatbot and still be able to provide your customers with a personalized experience.

Let’s look at these 2 methods in a bit more detail for our AI agents tutorial.

Training Open-Source AI Models

The world of open source is big and because of platforms like Hugging Face we now have access to open source AI models that have great flexibility and customizability. Building an AI model from scratch is a huge task and takes a lot of money and time to accomplish. However, for AI agent development open source AI models are quite developer friendly and you can train these models with your own data to create a tailored AI tool for your business. BERT, GPT and T5 are great examples of AI models that are available open source but platforms like Hugging Face have hundreds of AI models created by the open source community.

Now, let’s break this process down into steps so that we can understand it better.

1. Model Selection and Architecture

Depending on what kind of tasks you want your AI agent to perform you can choose the appropriate model architecture for AI agent development. For example, if you want your AI agent to understand the context of conversations better and then carry out tasks then a model like BERT would be the right choice. On the other hand, if you want your AI agent to generate coherent and contextually relevant text then something like GPT would make more sense. One great thing about open source AI models is that they are modular and you can adapt them to carry out a variety of NLP takes like sentiment analysis, summarization or questions answering through AI agent development.

2. Data Collection and Preprocessing

You need high quality datasets that are relevant to what you are trying to accomplish if you want to train open source AI models. Now, this data could be abstract and you also need to preprocess this data which involves processes like tokenization, cleaning and balancing so that the AI model can effectively learn from the data. In most cases you can use open datasets like Wikipedia or Common Crawl as starting points but you will need custom datasets if you want to improve your AI agent’s domain specific responses. Learning about these concepts is crucial if you want to learn how to build AI agents.

3. Fine-Tuning and Hyperparameter Tuning

The process is not the end of the line; you also have to fine tune your AI model on the target dataset. Additionally, you also have to set hyperparameters like learning rate and batch size to optimize your AI model’s performance. Why is this process so important? Because you need to refine the AI model to enable your AI agent to process and generate responses that are specific to your domain. For example, if you’re building an AI agent for customer support you can fine tune the AI model using customers’ conversations, FAQs and other relevant data to get appropriate responses.

4. Infrastructure and Scalability

The next thing to think about on how to build AI agents is the computing power you will need to train AI models. The training process requires substantial computing power and you will use GPUs or TPUs typically for this purpose. While buying physical devices that have this much computing power is not practical for most people, providers like AWS, GCP and Azure provide scalable computing solutions but this process can be expensive depending on how capable you want your AI agent to be. However, if you have the budget to train models and if you are worried about data security you can use private servers to train your AI models which will give you more control over your data during AI agent development.

5. Maintenance and Updates

Realistically, open source models require continuous updates if you want to improve their performance and so that they can adapt to new information. Most developers fine tune AI models periodically to make sure that the model stays current and regular evaluation and retraining on fresh data is a good way to make sure that you fix any errors or biased responses.

Integrating OpenAI’s APIs

Now, I can say outright that this option would be the way to go for most businesses looking for AI agent development. Using ChatGPT APIs makes your deployment pretty straightforward because you’re integrating third party APIs and you don’t have to worry about collecting, preprocessing or training with data. Most of the time, you can upload documentation for your customer service process and you can get some great results. But let’s dig a little bit deeper into the subject.

1. Rapid Deployment and Scalability

As we already discussed, using an API like the one for ChatGPT takes a lot of the grunt work out of the mix so that you can ship quickly. Your AI agent development process will be simplified and you don’t have to worry much about data. What you will have to build is the chatbot application itself with the interface so that your customers can chat with the bot. But after that OpenAI’s model will take over. One thing to note about the scalability is that you’re not using your own services for this. And OpenAI’s APIs cost money to work so as your user base increases you will have to pay for more OpenAI credits.

2. Prompt Engineering for Customization

Since you’re using APIs for AI agent development you don't have any flexibility at the model level. So what developers usually do is something called prompt engineering where you focus on tailoring the input prompts to align your AI agent’s behavior with the intended application. You basically create specific instructions to shape the model’s responses where you tell ChatGPT how you want it to handle customer conversations. Prompt engineering is a highly iterative process and you may have to test and refine your prompts to get the best results.

3. Cost-Effectiveness and Infrastructure Training

If you remember our discussion about needing high computing power to train AI models you can see why the API option is much simpler for AI agent development. It's OpenAI’s APIs so they will handle all the hardware and software maintenance and you don’t have to pay for training the model. If you’re on a budget and want to start automating your customer service operations quickly then integrating ChatGPT APIs will be the way to go.

4. Privacy Considerations and Data Handling

The big benefits of integrating third party APIs also show why there may be some concerns. And while you probably won’t have to worry about data security with an established solution like ChatGPT it's a good thing to keep this in mind during AI agent development. Since you’re using ChatGPT APIs you are sending data to OpenAI’s servers and reading through their privacy policies is a good practice. Some developers anonymize data before sending them through the API for processing but a good rule of thumb is to understand your project’s specific data control needs and decide on which path to take.

5. Maintenance and Automatic Model Updates

Maintenance and updates is where using external APIs will be really beneficial because you always get the current AI model for your AI agent development. It all happens behind the scenes and since OpenAI regularly updates its AI models you will have access to their capabilities as soon as updates are released. In other words your chatbot will improve as ChatGPT improves.

Exploring The Tools and Frameworks for Building AI Agents

Building intelligent agent software that’s powered by AI requires a diverse range of tools and AI agent frameworks. Because you have to handle everything from language processing and machine learning to data management and model deployment. We will now dive deeper into the topic and see the different tools and frameworks you can use for AI agent development.

Natural Language Processing Libraries

NLTK (Natural Language Toolkit): A very comprehensive Python library for building NLP tools, modules for tokenization, stemming, tagging and much more.

spaCy: A very fast and efficient NLP library that performs great in production environments. Very much optimized for tasks such as named entity recognition, dependency parsing and tagging parts of speech.

Transformers (Hugging Face): A great open source transformers library from Hugging Face for NLP model deployment and fine tuning. You can get pretrained models like BERT, GPT and T5.

Machine Learning and Deep Learning Frameworks

TensorFlow: It is a popular open source framework from Google for Creating and deploying machine learning models and is great for AI agent development.

PyTorch: A deep learning framework suited for research and experimentation with a flexible and dynamic computation graph. Great for conversational agents.

Keras: A high level neural networks API well known for its user friendly interface that runs on top of TensorFlow. Very effective for building AI agents when combined with pretrained models.

Reinforcement Learning Libraries

OpenAI Gym: It is an amazing toolkit for creating and comparing reinforcement training algorithms.

RLlib (Ray): A highly scalable reinforcement learning library which is built on the Ray platform. It allows developers to train RL models on large datasets across multiple machines for AI agent development.

Pretrained Model APIs

OpenAI GPT (via API): Probably the most popular of the APIs, the GPT API from OpenAI allows developers to create conversational chatbots quickly.

Hugging Face Model Hub: A great repository of open spruce and pretrained models for a variety of use cases.

Data Management and Annotation Tools

Labelbox: An easy to use tool that has simplified and collaborative annotations features for tagging, categorizing and labeling data.

Amazon AgeMaker Ground Truth: A great managed data labeling service which allows developers to annotate datasets with minimal overhead.

Interaction and User Experience

Dialog Flow (Google): A useful tool for building conversational interfaces which also enables voice and text based interactions in AI agent development.

Botpress: An open source platform for building chatbots and provides a great visual interface for creating conversational flows.

Let's Explore The Common Challenges in Building AI Agents

If you’re trying to build a conversational AI agent that you need to implement for tasks such as customer support you will have to address a few challenges in terms of technology, operations and some ethical considerations.

1. Data Quality and Availability

The quality of data and the quantity of data is very important when it comes to AI agent development because your AI agents have to perform well under real world situations. Low quality data that is inconsistent will lead to a chatbot that performs poorly.

2. Bias and Fairness

AI agents mimic human-like conversations and like any human their responses are solely based on what they have learned. Which means that AI agents can make mistakes and be biased if their training data is biased. You probably wouldn’t have this problem if you use something like the ChatGPT API because OpenAI has already trained their models on very large datasets. But when you’re doing AI agent development with open source models you have to ensure that your training data is balanced. Sometimes if that is not the case the AI agent can generate responses that are quite biased.

3. Understanding and Generating Natural Language

It probably is obvious by now that we use AI agents because we want to automate the customer service process and the chatbots have to handle conversations like humans. While AI is so advanced now that it can understand expressions and ambiguity in human language this is not always the case. Because through AI agent development you are creating an imitation of human conversations. Even advanced models like OpenAI’s GPT-4 can make mistakes in understanding nuanced conversations. While in everyday situations it might not be as big of a problem but you may have to do some prompt engineering to get the right results.

4. Real-Time Processing and Latency

If you want your AI agent to work efficiently for tasks such as virtual assistance and customer support you need real time responsiveness. In reality, large and more AI models require a lot of computing power and may have latency. Through AI agent development you have to find the sweet spots for balancing latency, accuracy, the size of the model, the architecture you deploy it on and so on.

5. User Experience and Human Agent-Interaction

User experience design and user interface design are crucial if you want your AI agent to be effective. Because the major purpose of an AI agent is to automate the customer support process and give users quick solutions to their problems. Your major goal should be to provide your users with a great experience through technical AI agent development as well as the design. A poorly designed user experience and user interface can cause a lot of frustration for users.

What Are The Different Applications of AI Agents?

Before thinking about how to build AI agents it is a good idea to understand how you can use AI agents in real world scenarios. Because AI agents are not just for generic customer service but they can be specifically trained to perform well in multiple industry use cases. Let’s look at some good examples.

1. Customer Service and Virtual Assistants

AI agents are great for customer service operations and the great thing about this technology is that you can provide 24/7 customer support for your customers. These chatbots can even provide personalized recommendations and answer specific user questions once you have trained them with good quality data relevant to your business through AI agent development.

2. Healthcare Assistants and Diagnostics

The healthcare sector has great applications for AI agents and many startups have already created chatbots that can give users a preliminary diagnosis for their symptoms. Now, it is not that you can replace healthcare professionals with AI agents but it does go miles when it comes to helping patients understand their symptoms and what kind of practices they can follow to keep healthy. But that’s not all, with modern AI solutions you can even analyze images like scans and the AI can interpret what it means and this gives patients a quick overview of what they are dealing with 24/7.

3. Financial Services and Fraud Detection

AI agent development has very useful applications in the finance industry because this is an area where people have lots of questions. AI agents can help customers with everything from customer support and identifying fraudulent patterns to creating and managing your portfolio of assets based on historical data. Many financial institutions have already implemented AI agents for customer support and some banks have even gone so far as to switch their IVR system to intelligent AI agents that can receive voice inputs and respond according to the customers’ speech.

4. Supply Chain Management and Logistics

AI agent development has a lot of potential in the supply chain management and logistics space but the most obvious of which might be with consignment tracking, timely alerts and to address grievances. AI agents can also help delivery personnel with finding the best routes to their destination. Although route optimization usually happens behind the scenes usually on the map itself to aid in a fluid user experience. But chatbots can make supply chain management very efficient because you don’t have to go through multiple menus to find things like inventory details. All you have to do is ask the AI agent.

5. Personalized Education and Tutoring

AI agent development can be a game-changer for the education sector because you have access to knowledge literally at the end of your finger tips. What’s more is that AI agents can even be trained to act as a personal tutor where the AI agent can provide knowledge, evaluate your progress and give you suggestions on how to improve. Generative AI solutions like ChatGPT have been aiding students since their inception but AI agents specifically trained for tutoring can even provide learners with entire courses without having to leave the comfort of their homes.

Wrapping Up Our Guide On How To Build AI Agents

We’ve reached the end of the line for our AI agents tutorial but I hope that now you have a much deeper understanding of how AI agents work and what it takes to build a good AI agent. While creating AI agents is great for automating your client facing processes it is also important to remember that the customer experience should take priority always. Creating and training chatbots is one part of the puzzle but a bigger part of the puzzle is making sure that your customers can seamlessly use your AI agent without feeling like they are speaking to a computer.

If you’re a business owner that wants to enhance your customer experience and increase your customer satisfaction then book a call with our expert consultant today to get started.