AI Agents: Your New Co-worker in the Digital Age
An agent is essentially a tool that performs specific tasks, receives feedback, and refines its processes accordingly. The fundamental distinction between human labor and AI agents lies in intelligence. Humans possess the ability to think critically and adapt to various situations beyond their existing knowledge. In contrast, AI agents are constrained within their predefined knowledge boundaries, making them suitable for specialized tasks and human assistance. Additionally, AI agents excel in virtual environments. When evaluating new technologies as trends, three crucial factors come into play:
- Advancements in interaction methods
- Enhancements in information access methods and
- Optimized utilization of computational resources.
Advancements in interaction methods with AI Agents
Earlier the computer was accessible through code only and only people who knew basic programming could perform tasks on it. Later came era where we got better User Interfaces, which made the computer technology more scalable. Even the person who has no understanding of tech can use it to simplify his daily life. Going ahead AI Agents could build an eco-system which would make Computer technology even more accessible. Imagine Jarvis from Iron Man movie available in every household. Any person would be able to carry out task with just vocal orders to machine. If we take Improving user experience and seamless interaction as a trend, then AI agents tend to rule the future.
Enhancements in information access methods with AI Agents
Agents are basically piece of software developed to perform specific task and powered by natural language processing technology. Earlier when you had to understand something you had to go through entire pdf or search for certain keyword in pdf and go through all the occurrences. But nowadays there have been several tools available that allow you to interact with your pdf in a conversational format. You can ask the Agent to summarize the entire pdf containing hundreds of pages and you will get results in seconds. Going further AI Agents will have capacity to make information access much simpler for variety of use cases.
Optimized utilization of computational resources
Right from room sized computers to current mini computers the technology advancements have allowed to do more in lesser size. AI Agents generally run on GPUs rather than CPUs. This allows them to break down certain task and perform them in parallel instead of sequentially. This simply doesn’t mean that GPU’s are much more powerful than CPU’s. It completely depends on our use case. Imagine that you need to lay bricks in one line and you have two options.
Option 1 – Hiring Einstein
Option 2 – Hiring 100 average people with average intellect.
For this particular use case of laying bricks hiring 100 people looks more meaningful as they have to carry out simple repetitive tasks at parallel. But imagine you need to solve a complex mathematical problem that can have answers of existence of universe, who would you hire? 100 average people would obviously reach nowhere closer compared to what Einstein can solve. While AI agents are not the triggering element of the advanced computational technology but yes they are implemented on the most optimized computational resources of current time.
Types of AI Agents
Generally AI Agents can be divided into five broad categories, let’s understand them in detail here:
- Reactive Agents
These agents consider what happens in current time and can be understood as simple rule based agents. There is no impact of past events or environmental factors in decision making. Imagine Automatic door opening sensors. They have nothing to do with who the person is, whether he has entered in past or not, what is happening in surrounding. One simple task that they are built for is, identify the presence of someone in front of door and open it. That’s it, nothing more than that.
- Adaptive Agents
These agents do have the understanding of their environment. Based on the changes in environmental factors there response is changed. Also, they try to store and learn from the occurrences of events in past. Imagine Self driving cars as an agent. They have an understanding of the surrounding environment. And they continuously make changes in speed or rotation of steering based on roads and traffic.
- Purpose driven Agents
These could be understood as goal driven agents. They constantly work to reduce the distance to reach their next goal. They are best to implement where the environment is evolving continuously. Imagine computer chess bot as an agent. They try to figure out the best move after every iteration to reach their end goal, which is to check mate the opponent.
- Rational Agents
These agents are designed to maximize the satisfaction of the user or to reach at highest level of utility. They revolve around a utility function which could consider several metrics impacting user satisfaction. Imagine Netflix recommendation engine as an agent. They continuously work to provide the best movie recommendation based on past decision of users and their actions. They are designed to provide the relevant movie from its vast library based on user profile, which will eventually lead to increased customer satisfaction.
- Adaptive Learning Agents
These agents continuously evolve to improve their performance by leveraging understandings gained over time. It is like continuously adding the context to the internal decision making framework to improve future decision making. Imagine virtual personal assistants like Alexa and Siri as agents. They continuously learn from the interaction with the users to improve their performance. They continuously build understanding by tracking speech patterns, User preferences and needs. With time this approach allows them to deliver more assistance in getting tasks done.
Types of AI Agents
While there are already existing AI functionalities in Microsoft Office or Google Workspace, they don’t provide an exhaustive automated experience. In coming time there will be increasing number of Agents build and bundled together to provide the best end to end experience to the user. Most of the large language model builders have started to build an ecosystem of agents which allow users to carry out specific tasks. Also there have been open source and private platforms rising which allow you to build Agents that could automate your recurring day to day tasks. Organizations should leverage this technology to increase employee efficiency and streamline workflows.
