Did you know that 9 out of 10 businesses put their trust and investments in various forms of artificial intelligence?

AI agents have become a widespread solution for workflow optimization, more efficient interactions with customers, accurate analysis, and many more purposes. Unfortunately, many companies integrate artificial intelligence without fully understanding how it works and how to utilize it for actual results. Start-ups and SMB companies risk spending their budgets without getting what they expect. This article will show you how to build an AI agent that can bring real benefits; we will hint at what to consider and what challenges you may face. 

What Is an AI Agent?

An AI agent is an autonomous computer program created to help people with routine tasks, enhancing human teams’ capabilities to a new level of precision and effectiveness. It is an advanced technology that can perceive the environment, collect information, react to input, make decisions, and take actions to achieve preset goals. 

If simple bots can only act according to predefined rules without actual thinking and learning, artificial intelligence can adapt, learn from experience, and operate without constant human control. 

What kind of tasks can an AI agent perform?

Artificial intelligence is widely used for many purposes: according to Statista’s insights, 71% of companies utilize them for data security enhancement, 67% leverage web and social media analytics, and 66% use them as call center chatbots. Let’s look at a few more examples of AI use in various industries.

• Global use: Virtual assistants and chatbots can simplify users’ lives by answering basic questions, scheduling appointments, and helping with information searches. AI-powered solutions are often used for system controls like vehicles and smart homes;
• Customer support: AI agents can become game changers for customer support departments by handling customer queries, processing refunds and complaints, routing requests, and offering personalized support;
• Content creation: Generative AI has become popular thanks to its ability to create content like articles, music, and video materials and improve the quality of media files. Artificial intelligence can efficiently assist during code writing and design creation;
• Automation: AI can streamline a large portion of repetitive tasks. They can respond to frequently asked questions, detect defects during various manufacturing stages, assist with troubleshooting, provide order status, and much more;
• Forecasts: AI-powered predictive analytics has become a significant tool for many industries, including finance, retail, sports, travel, etc. It helps predict possible outcomes, peak periods, stock market trends, and marketing campaign results.

How Do AI Agents Work?

AI agents’ work has three stages: perception, decision-making, and action. They utilize large language models (LLMs) to interpret and understand the user’s input and decide which actions work best for a specific goal. 

Let’s look closer at the four main functions that make artificial intelligence agents so effective:

• Input processing: AI accepts information from available sources, including sensors, databases, or user input in text or voice commands;
• Decision-making: Applying large language models (LLMs), the AI model understands the input and chooses the course of action to achieve the required goals most efficiently;
• Action execution: Once the decision is made, the AI can perform the required actions. Then, natural language processing generates a response to the user;
• Learning and adaptation: Thanks to learning and adaptation, artificial intelligence can use its experience in the future to understand what impacts success and provide required adjustments.

What Are the Types of Agents in AI?

If you want to build a workable AI model, you need to understand the differences between their types. So, to make your choice easier, we have prepared a short review for each of them. 

Simple reflex agents

This type is the simplest one and works best with tasks where an agent can decide based only on the reaction to the current situation. They do not learn continuously or consider past experience as a base for decisions. Reflex agents are fast and simple to realize. For example, you can use them for simple antivirus programs, automatic door systems, temperature regulations, etc. 

Model-based reflex agents

You can consider these agents the next level because they track previous experiences and better understand the consequences of their actions. Model-based reflex agents use transition and sensor models to perceive the world and understand what is happening around them. This type of reflex agent can adapt to changing conditions, but, as a result, it requires more resources for operation. For example, automated traffic regulation systems can evaluate current and past data about the roads. 

Goal-based agents

The mission of a goal-based agent is to achieve a specific goal most effectively. They do not just react to situations but also think ahead and decide what actions to take for a specific outcome. Goal-based agents are more flexible when you need to achieve a complex target and have better adaptation to changing conditions. However, keep in mind that they also need more complex planning algorithms and preset goals. You can find goal-based agents in logistics systems that optimize delivery routes.  

Utility-based agents

These agents go beyond simple goal achievement; they evaluate the success of various actions. Using utility functions, agents measure how good one or another option is and choose the most effective one. As a result, utility-based agents find not only a random decision but the best one possible under current conditions. They are very flexible and can easily adapt to changing environments. For example, trading algorithms on the stock market evaluate potential outcomes and risks of various investments to choose the best strategy. 

Learning agents

Here, we come to the most advanced agents with the highest adaptation level and flexibility. They get a basic set of skills and knowledge during training, and further, they can learn, evaluate experience, and adjust their behavior. The built-in coach ensures continuous learning and improving performance for better results next time. Their work is based on machine learning and neural networks. Self-learning and the ability to perform complex tasks make them suitable for most modern AI systems. For example, voice assistants Siri and Alexa are also examples of learning agents adapting to user preferences. 

What Are the Benefits of AI Agents?

Hygge software offers professional AI development services, so we know from our experience which advantages AI implementation can bring. Let’s check the main benefits of use and how AI can impact your current workflows.

Information analysis and decision-making with a new level of speed

Information research and preparation for informed decision-making can be extremely challenging and time-consuming. Artificial intelligence removes such problems by going through huge amounts of data without losing quality and speed. It will analyze, evaluate, and create a key insight, summarizing all required information. As a result, the company is provided with all the necessary facts to make the right decision.

Great productivity boost

Teams’ productivity is the key to success in any industry, and artificial intelligence can significantly boost it by automating routine tasks. AI implementation frees your human agents from answering repetitive questions endlessly. They can handle FAQs, manage schedules, assist with troubleshooting, help your teams with information search, and optimize workflows. As a result, humans can have more time for complex and creative tasks. 

Enhanced CX

Automation enables round-the-clock assistance with basic inquiries. AI-powered solutions help to speed up the responses and reduce the pressure on human support teams. A new level of personalization based on preference analysis and history of interactions enables a personal approach to each customer. 

Accelerated software development

AI-powered solutions can actively assist during code writing, testing, documentation generation, and debugging. AI-powered help for development teams not only streamlines the process but also helps to maintain a high quality, reducing the chance of errors and mistakes.

Improved data quality and consistency

AI automates data cleaning, validation, integration, and enrichment processes. It can help detect, identify, and fix errors, including spelling mistakes, duplicates, and other inconsistencies. Artificial intelligence can provide accurate cross-checking with external sources and detect anomalies. As a result, an AI reduces the chance of manual data entry mistakes.

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How to Create an AI Agent: Two Main Approaches

There are two main approaches to AI agent development: creation from scratch and using existing frameworks. There are many discussions about the advantages of each one, but the choice always depends on your purposes and goals of implementation. Let’s review each approach and see what pros and cons it can provide.

How to build an AI agent from scratch

You will need high software engineering and machine learning expertise to build an agent from scratch. However, it is a complex and time-consuming process that can offer a highly customizable AI model.

Our services include custom development of AI models, and we believe that creation from scratch works best when you need full control over all the functionality. With the brand-new model, you can get any desired design and a high level of customization to fit specific tasks.

Pros: 

• The AI model entirely belongs to your company
• The model is trained and tailored specifically for your business needs
• Full control over the model
• Unlimited customization possibilities

Cons:

• Requires experts with specific technical skills
• The development process takes time
• Building from scratch requires more resources

How to create AI agents with the help of existing frameworks

Some businesses need AI models for quite common tasks, and customization is not a crucial factor for them. Using existing frameworks as a base for AI models can be a more cost-saving option. Pre-trained AI models need less time for training and can be adjusted to business needs. You take a pre-built model and adjust it to your needs. Usually, they offer standard AI functions and utilize advanced language models for core tasks. Among them are Microsoft Autogen, LlamIndex, LangChain, and CrewAI. 

Pros:

• Less expensive
• Offer access to advanced LLMs
• Development requires less time and technical skill

Cons: 

• Less customizable
• Dependence on framework provider
• Will not suit for non-common tasks

How to Build an AI Agent Step By Step

The AI model development process is complex and challenging, so we have prepared seven easy steps to follow for successful AI implementation to make it simpler and less stressful for you.

Step 1: Define your agent’s purpose and environment

First, you must decide what you want to achieve with AI implementation. Clear goals and expectations will help to set the right course for the entire development procedure. It is important to consider the environment, future functions, and purposes of interactions.

We recommend answering a few questions:

• What is the purpose of an AI implementation?
• Where are you going to apply it?
• How much autonomy should it get?
• What is the desired outcome?
• What kind of work will it need to perform?

Step 2: Collect, clean, and prepare essential data

You must feed high-quality, diverse, and accurate data for effective model training. This step needs you to decide which sources you want to use for training. Will it be structured data from your company’s databases or unstructured data from emails or devices? It is essential to check everything, remove duplicates, fix missing values, and standardize formats. 

Step 3: Assemble a skilled development team

The quality and capabilities of your future AI model will directly depend on your development team’s expertise. Professionals will need to build and train AI models, so we recommend making sure that they have all the required knowledge and expertise in the following areas:

• Data Science
• Machine learning engineering
• Software engineering
• UI/UX design
• DevOps

Step 4: The choice of tools and technologies

You can choose tools and technologies based on the tasks that you need your agent to fulfill. ML frameworks will serve as a base for your project so that you can choose one of the popular ones, like PyTorch or TensorFlow, depending on your project’s difficulty. 

If your future agent will need to interpret and respond to human language, we recommend you consider natural language processing libraries. Computer vision is a must-have for handling visual tasks. 

Next, you must decide if you want a cloud-based agent or an on-premise one. An on-premise platform offers better security, while cloud options enable bigger scalability potential. 

IDEs, also known as integrated development environments, can be a helpful solution when you want to speed up your coding and testing procedures.

We recommend making the tools with more active communities your priority, as, in this case, you will have more chances to find detailed guides or troubleshooting advice.

Step 5: Design the AI agent

One of the most complex parts is that development impacts how your agent will work and how it will look.

You can consider one of the two architecture options:

1. Modular, where you develop each AI agent module separately and then integrate them all. It is easier to provide updates, scaling, and debugging.

2. Concurrent, where the system performs many tasks simultaneously. This option will work better if you need an agent to handle multiple real-time operations.

Once you know what you want from your future architecture, you can choose which core functions to add. It usually depends on the core tasks of your agent. They may include data input, information processing, decision-making process, response generation, etc. 

Imagine how you would prefer users to interact with your agent. Will it be through chatbots, commands, menu interfaces, or something else?

We also recommend thinking about feedback mechanisms, as they will significantly help to understand how your agent works and if it needs any adjustments.

Step 6: Test the AI agent

Testing is an extremely important step, as not all errors can be visible simultaneously. The more training and testing your model can get, the better. For example, you can apply A/B testing with various user groups to get all-encompassing feedback about an agent’s convenience and usability. Don’t be afraid to add unexpected requests, use sarcasm, or use several inputs or incomplete information to make your agent learn how to improve its interpretation skills.

Step 7: AI agent deployment and further monitoring

Once the testing is completed successfully, you can deploy your AI agent. But remember that deployment is never a final step, as artificial intelligence needs regular monitoring to ensure its effectiveness. Even with the best testing results, you still may find some functions you want to improve. The AI-powered solution may need updates when your company adds new products or users apply new terminology. Monitoring will help to detect inefficiencies and fix them.

Challenges to Overcome When You Build an AI Agent

In our experience, the most common challenges you may face during AI agent building are the following:

• Data quality and quantity: AI model development requires big and versatile data for training. Biased or incomplete data can cause inefficiencies in the agent’s functionality;
• Algorithm selection and model complexity: The choice of algorithm will impact all future agents’ work. A simple one can be not efficient enough, while a complex one may be too inconvenient;
• Real-time processing: Agents that must process data in real time require edge computing and algorithm optimization; otherwise, they will not operate fast enough;
• Scalability: You need to understand that scalability is not a pre-built option for any agent; it can be difficult to handle growing workloads for non-cloud-based ones;
• Ethical and regulatory compliance: Fairness and transparency can be challenging, and your AI agents must follow industry regulations and privacy laws like CCPA and GDPR;
• User interaction and experience: If your AI solution has a too complex design or responses that are too far from human-like, users will struggle to use it, and the adoption rates will be low.

As you can see, development can cause a bunch of challenges. Still, professional development teams know how to deal with them and turn every challenge into an opportunity and a reliable solution. 

Conclusion

An AI agent can be a very helpful solution for workflow streamlining, but only with professional technology and a business approach that can apply all AI capabilities. At Hygge Software, we know how to make it right and empower your company with advanced modern technologies based on artificial intelligence. Do you want to reduce costs without losing efficiency? Let’s discuss which AI agent approach will work specifically for you.