34% of customers prefer working with AI agents to avoid repeating themselves – States a 2025 report by Salesforce. Working alongside a human is great, as we can be more empathetic than robots, these agents in AI are taking businesses by storm.
Whether it’s handling repetitive tasks or handling huge amounts of data, they can handle it. And with minimal human intervention! With an increase in AI adoption, 72% of companies have started using these agents for their businesses (As per McKinsey and Company).
By the time you finish reading this blog, you’ll know everything that there is to know about them. Continue reading to find out more.
What are AI Agents?
If you’ve already interacted with a chatbot on a website, you already have a basic idea of what they are. These agents are trained to interact with humans and help us receive the piece of information we’re looking for.
However, these agents are more advanced as they use natural language processing (NLP) and can help you make decisions on complex information. These tools are used in different situations like software design and IT automation.
5 Different Types of AI Agents
According to IBM, there are 5 different types of agents in AI. They are –
i) Simplex Reflex Agents
These are the most basic forms of agents that don’t hold any memory, nor do they interact with other technologies. They’re pre-programmed to perform certain actions only.
Example – These agents can enable a thermostat to start heating up a room around 8 pm, during the winter season.
ii) Model Based Reflux Agents
These agents aren’t restricted to the information they’ve stored. They can operate based on the environment around them.
Example – A robot cleaning dust on the floor can detect furniture in its way and can move around it. It’s trained not to clean areas that are already clean and find their way around the furniture.
iii) Goal-Based Agents
These agents make decisions based on how certain actions can help them reach closer to their goals. This level of planning is effective when compared to the earlier 2 models.
Example – A GPS system will recommend you a route that can help you reach your destination faster. Their condition action rule states that if a faster route is found, the AI agent will recommend that instead.
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iv) Utility-Based Agents
This AI agent weighs the alternatives to reach your goal and maximize the utility. A utility metric is assigned that can measure the effectiveness of an action and the progression towards the goal.
Example – The navigation system can also tell you how much fuel you’d save if you took a specific route or the amount of time you’d save.
v) Learning Agents
Learning agents learn along the way, based on their previous interactions. They can work well in unfamiliar environments and new information is added to their knowledge base automatically.
Example – These AI agents are widely used in e-commerce industries. Based on the customer’s interactions, they can recommend products based on their shopping history.
6 Steps to Build Your AI Agent
Now that we know 5 different types of agents in AI, we learn how you can build your very own. And it’s fairly easy.
1) Define Your Objective
Clearly define the purpose of your AI agent. What type of functionalities would you like it to have? It’s advisable to align your business goals with these agents so that you get more accurate outcomes like faster response times.
2) Choose the Right AI Model
There are different AI models for you to choose from. Options include rule-based, deep learning, utility-based, goal-based, simple reflux, and model-based reflux. Weigh the pros and cons of each before you make your final decision.
3) Gather Your Data
Training these agents in AI can be time-consuming, but don’t skip this step. Gather all the necessary information like customer data, transaction history, and industry-specific datasets. Additionally, organize and label these data so that your agent can deliver accurate responses.
4) Develop the AI Agent
Take advantage of tools like TensorFlow, PyTorch, or OpenAI’s GPT to bring your agent to life. Opt for programming languages like JavaScript or Python to add the functionalities you want.
5) Train and Test your Agent
Conduct a thorough test of your AI agent to find loopholes. You need to ensure that your agent understands these inputs correctly. Fine-tune the performance even more, so that they can handle real-world interactions effortlessly.
6) Deploy and Monitor
Continuously monitor your AI agent even after it’s deployed. Get regular user feedback to fix bugs that you may have overlooked and continue updating your agent for enhanced accuracy.
3 Examples of AI Agents
1) Customer Support Automation
AI agents like chatbots can help in answering customer queries and provide 24/7 support. They can be trained to personalize the interactions based on the query and escalate serious issues as and when it’s needed.
2) Healthcare Assistants
According to a 2025 report by Salesforce, 39% of customers use AI agents to schedule appointments on their behalf. These agents can also be used to answer health-related queries.
3) Retail Industry
24% of consumers are comfortable with an AI agent helping them shop online. Apart from providing personalized recommendations, they can help you in managing your supply chain as well.
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3 Challenges Faced by AI Agent
No technology is perfect, no matter how advanced they are. The same theory applies for agents in ai. It’s important to know these 3 challenges before you start making your own.
1) Data Privacy and Security
If not handled with care, these agents can be hacked. Therefore, add another layer of protection on them and ensure they follow compliance with data regulations.
2) Integration with Existing Systems
This can be an issue, as these agents may not integrate with your legacy systems. Thankfully, API-based solutions can help in the transition.
3) Bias Issues
These AI agents work based on the information you feed. This can lead to inaccurate or biased responses. Regularly conduct audits to mitigate this challenge.
Wrapping Up
To sum up, everything that we read, about these AI agents can be beneficial for your business. But you should know about the potential challenges that you might face. When you follow a structured approach, you will maximize the results.
However, if you need help in getting started or have questions – Get in touch with Machin Learning Experts. With 23 years of experience and having worked with reputed brands like PVR and Haldirams, they can help you deliver the results you have dreamt of.
