AI Agents: Everything You Need to Know in 2025

AI has come a long way, and one of the developments in this field is AI Agents! Let's make it clear AI agents are poised to redefine the way operations are handled. I think these AI agents are becoming important in many sectors, from automating tasks to making high-level decisions. Look at the share market.

According to the research done by GRAND VIEW RESEARCH, the market size of AI gents was valued at USD 3.86 billion in 2023, and it is expected to grow by over 45 % from 2024 to 2030.

Well, this blog will cover everything you need to know about AI agents, including their definition, functionality, types of AI agents, and how it can benefits your business.

What are AI Agents?

AI agents are software programs that use AI to perform specific tasks autonomously. Easy right? They interact with their environment, process large amounts of, make decisions, and take actions based on predefined rules. Unlike normal automation tools, these AI agents can learn, adapt, and improve over time, making them valuable for everyone.

For example, the AI chatbot we use today is focused on taking a specific command and getting a response from the LLM, but an AI agent will be able to take further action based on the results of the LLM  response, with or without any human involvement.

The AI agent also uses the results to formulate the next action that it should take. For example, self-driving cars navigate around obstacles on the road based on data from multiple sensors.

How Do AI Agents Work?

Now we know what AI Agents are and what they do. Let's see how AI agents work. AI agents are designed to function autonomously by interacting with their environment through sensors, API, or direct data feed as well. Here’s a small breakdown of how they work.

1. Determine Goals
   
   The AI agent receives a specific instruction or goal from the user. It uses the goal to plan tasks that make the outcome more relevant and useful to the user. The AI agent will break down the goal into several smaller tasks. Then, the AI agent performs those tasks based on specific orders or conditions.
   
   
2. Acquire information
   
   AI agents need information to act on tasks they have planned successfully. For example, The AI agent must extract conversation logs to analyze customer sentiments. As such, AI agents might access the internet to search for and retrieve the information they need. In some applications, an intelligent agent can interact with other agents or ML models to exchange information.
   
   
3. Implement Tasks
   
   With sufficient data, the AI agent implements the task at hand. Once it has done a task, the AI agent removes it from the list and goes to the next one. In between task completions, the agent evaluates if it has achieved the designated goal by seeking external feedback. During the process, the agent might create an act on more tasks to reach the final outcomes.
   
   

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Type of AI Agents

AI agents come in different forms, each designed to meet specific needs. Here are the most common types.

1. Simple Reflex Agents

‌‌‌‌Simple Reflex agents are basically AI agents who will react based on a set of predefined rules. They don't have any memory or ability to learn. They follow a stimulus-response approach and are best suited for immediate reactions like chatbots.

2.  Model-Based Reflex Agents

Model-based reflex agents, on the other hand, consider their past experience. They keep track of the state of the world they’re operating in. They use machine learning techniques to adapt and improve over time.

3.  Goal-Based Agents

As the name suggests, Goal-based agents work towards achieving a specific objective. Their actions and steps are focused on the goal. A game of chess would be a good example.

4.  Utility-Based Agents

Utility-based agents are very similar to goal-based AI agents but with an extra component that makes them a bit different. These agents do not act based only on achieving goals, but they also try to find the best way to achieve the goal. The utility function maps each state with a real number to check how efficiently each action will be taken to achieve the goals.

5. Learning Agents

Finally, at the top of the list, we have learning agents. These AI agents can learn from the environment and improve over time. They get better at every task by practicing it. Basically, the more interaction it makes, the more skillful it becomes.

Components of an AI Agents?

You might have guessed by now that AI agents have complex structures and complements. I have broken it down so that it is as easy for you to understand as possible. So get right into it.

1. LLM
   
   So, LLM serves many roles for AI Agents. It can guide the process or make some important decisions based on the inputs it receives from the user or other applications. On top of that, over time, you will see AI agents leveraging multiple LLMs or machine learning models to achieve the specific goals of the agents.
   
   
2. Data
   
   For any AI application, data is important, either the application data for the programming elements or inference data from the LLM.
   
   
3. Governance
   
   This refers to rules and parameters that constrain the AI agent. This will include policies, roles, and credentials.
   
   
4. Integrations
   
   One of the important aspects is the elements of connectivity and data provenance, which can communicate with different applications and services.

What are the Benefits of AI Agents?

You might be asked what the benefits are. Well, AI agents can do many things, such as improve customer service and decision-making. Here are the primary benefits.

1. Task Automation
   
   AI agents can automate complex tasks that would otherwise require human resources. This translates to goals being reached inexpensively, rapidly, and at scale. In turn, these changes mean humans don’t have to provide direction to AI Assistants for creating and navigating their tasks.
   
   
2. Greater Performance
   
   Multi-agent frameworks tend to outperform singular agents. This is because the more plans of action are available to an AI agent, the more learning and reflection occur. An AI agent incorporating knowledge and feedback from other AI agents can be useful for information synthesis.
   
   This backend collaboration of AI agents and the ability to fill information gaps are unique to agentic frameworks, making them a powerful tool and a meaningful advancement in artificial intelligence.
   
   
3. Quality of Responses
   
   AI agents provide more comprehensive, accurate, and personalized responses to the user than traditional AI models. This is important to us as users since higher-quality responses improve custom experience.
   
   

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Why AI Agents Matters for Business

We know an AI agent can be a powerful tool. Let's see if this can help business as well.

1. Agents have Clear Purpose
   
   Copilots, and chatbots let users ask anything, even if it's beyond what the system can do, and no action might be taken. On the other hand, agents follow a specific set of steps or use certain tools to complete tasks. This makes it easier to measure their performance against business goals.
   
   
2. Non-developers can Create and Manage Agents
   
   With no-code or low-code tools and simple prompts, AI agents can be built quickly, even by non-technical people. One good example is Salesforce’s new AI agent platform, which has a number of cool examples where a non-technical person can build their agents.
   
   
3. AI Agents can give AI better Context for Text
   
   There are no issues that AI might give vague or wrong answers to. We all have experienced that. However, with AI agents, you can set clear prompts and use well-defined data and services, so the AI gives more accurate and relevant responses than public chatbots.

Use Cases of AI Agents

Numerous industries automate complex processes and Improve decision-making. Here are some notable use cases.

1. Customer Experience
   
   There are tons of real-world use cases of AI chatbots, but AI agents can be integrated into websites and apps to improve customer experience instead of virtual assistants, provide mental health support, and also simulate interviews and other related tasks.
   
   
2. Healthcare
   
   AI agents can be used for many healthcare applications. Multi-agent systems can be particularly useful in treatment planning and managing drug processes. This system not only saves time but also allows medical professionals to focus on urgent tasks.
   
   
3. Emergency Response
   
   Also, in the case of natural disasters, AI agents can use deep learning algorithms to get information about users on social media sites that need rescue. The locations can be mapped to assist rescue services in saving more people in less time.
   

What are the Challenges of Using AI Agents?

AI agents are helpful software technologies that automate business workflows for better outcomes. That being said, organizations should address the following concerns, which I think you should keep in mind when you are deploying AI agents for business.

1. Data Privacy Concerns
   
   As we all know, data and privacy are among the most important concerns for everyone. Organizations should be aware of data privacy requirements and employ necessary measures to improve data security posture.
   
   
2. Ethical Challenges
   
   In certain circumstances, deep learning models may produce unfair, biased, or inaccurate results. Applying safeguards, such as human reviews, making sure customers receive helpful and fair responses from agents deployed.
   
   
3. Technical Complexities
   
   Implementing advanced AI agents requires specialization and knowledge of ML. In this case, developers must be able to integrate machine learning libraries with software applications and train the agent with enterprise-specific data.
   
   
4. Limited Compute Resources
   
   Training and deploying deep learning AI agents requires substantial computer resources. When organizations implement this agent on-premise, they must invest in and maintain costly infrastructure that is not easily scalable.

Conclusion

As AI agents evolve, their impact on business operations and customer experiences will grow exponentially. Adopting AI agents is no longer a choice for businesses looking to stay competitive in 2025—it's a necessity. From improving efficiency to providing personalized services, AI agents are shaping the future of technology and industry.

If you are looking for an AI development company, we at Third Rock Techkno have a decade of experience in custom software development, and we can help you integrate AI or develop an entirely new platform custom for your business. Contact us now!

FAQs

1. What industries benefit the most from AI agents?
   
   Industries such as healthcare, finance, e-commerce, and customer service see significant benefits from AI agents due to their need for data analysis, automation, and real-time decision-making.
   
   
2. How do AI agents learn from their environment?
   
   AI agents use machine learning algorithms to analyze data and learn from their interactions with the environment, improving their decision-making capabilities over time.
   
   
3. Are AI agents difficult to implement?
   
   Implementing AI agents requires planning and the right tools, but many AI solutions offer scalable and customizable platforms for businesses of all sizes.
   
   
4. How secure are AI agents?
   
   AI agents can be highly secure if implemented with robust security measures, including encryption, access controls, and regular updates to protect against vulnerabilities.