A Complete Guide on AI Agents

A Complete Guide on AI Agents

Imagine having an AI assistant to automate your entire workflow with no errors — AI agents can do that. AI agents use large language models such as GPT to check off tasks and complete a goal. Thus, you can use them to automate tasks and outsource complex cognitive goals. 

Currently, these agents are evolving quickly with new models and frameworks. They’re reliable, fast, and efficient. In this article, we will share all about AI agents, their types, and benefits you can avail from them!

What are Artificial Intelligence(AI) Agents?

A man touching 3D Ai hand showing what are AI Agents

An artificial intelligence (AI) agent is a system that is programmed to autonomously perform tasks on the behalf of someone. It can help design a workflow and utilize available tools to complete certain tasks. 

AI agents use the advanced natural language processing techniques of large language models (LLMs). Thus, they possess a wide range of functionalities such as decision-making, problem-solving, interacting with external environments and taking actions. 

Since they use advanced natural language processing techniques, they can perform complex tasks. These tasks could range from IT automation to conversational assistants, depending on the context.

How Do AI Agents Work?

An AI agent uses its sensors to gather data, control systems to think through various solutions. Then, its actuators take action, gather feedback on it, and keep tracking the progress until the final goal is met. 

Let’s have a look at a step-by-step process of a goal-based AI agent.

  1. Goal Initialization: When you enter an objective, the agent passes the prompt through its core LLM (such as, GPT). Once it returns the first output of its internal monologue, it shows that the agent has understood your goal. 
  2. Task List Creation: Based on your goal, the agent will generate a set of tasks, trying to understand in which sequence it should complete them. Once the agent figures out, it’ll start searching for information. 
  3. Gather Information: Since the agent can use a computer exactly like you, it starts gathering information. Sometimes, these agents outsource tasks to other AI agents for better decision-making. 
  4. Learning Phase: The agent uses all the stored data to improve its strategy moving forward. As the tasks are completed, the agent gathers feedback to assess how far it is from the goal.
  5. Feedback Loops: The agent keeps gathering feedback, creating new tasks, and repeating the same until it finally reaches its goal.

An AI agent gathers feedback from both internal and external sources. However, it cannot stop at a certain point and may end up with infinite feedback loops. Thus, human monitoring is important in such steps.

Essential Components of an AI Agent

3D view of Essential Components of an AI Agents

An AI agent has 5 essential components that help it complete a task, or a series of tasks. Here are all the components an AI agent uses:

  • Sensors: They allow the agent to perceive its environment using cameras, microphones, or other sensory devices.
  • Actuators: Tools like motors, speakers, or other output devices help the agent interact with its environment.
  • Knowledge Base: An AI agent’s internal memory is its knowledge base that stores all the data, set of rules, and new information. 
  • Decision-Making Mechanism: The mechanism allows the agent to choose the best action plan to meet a goal. 

Learning Systems: The learning mechanisms allow the agent to learn from their actions, take feedback, and create new tasks to meet their assigned goal.

5 Types of AI Agents

AI agents can be developed to gain different levels of capabilities. Simple agents may be chosen for straightforward goals, without much complexities. Here are 5 main types of AI agents, from simplest to most advanced:

1. Simple reflex agents

Simple reflex agents, as the name suggests, are the simplest form of Artificial Intelligence agents. This type of agent functions on a set of predetermined rules. In simple words, these agents perform actions when certain conditions are met. 

They do not hold any memory, nor do they interact with other agents to get information. Thus, in case they encounter a new situation, they cannot respond properly. This makes simple reflex agents effective only under predetermined situations. 

Example: Thermostats are classic examples of simple reflex agents. They take action when a room’s temperature rises above or falls below the set temperature.

2. Model-based reflex agents

Model-based reflex agents use both the current data and their memory to maintain an internal model of the world. Every time the agent receives new data, the model is updated. Therefore, its actions depend on its predetermined rules, model, previous precepts and current state. 

Unlike simple reflex agents, these AI agents are effective in partially observable and changing environments. However, they still hold a limit with their set of rules. 

Example: Self-driving cars are an example of model-based reflex agents. It uses its sensors to perceive its environment, takes the information to update its internal model, and predicts likely outcomes. Thus, making decisions to ensure the cars safety and efficiency.

3. Goal-based agents

As the name suggests, goal-based agents have a set of goals to fulfill. These agents have an internal model of the world. So, they search for action sequences that can fulfill their goal, plan them, and then act on them. Since goal-based AI agents pre-plan every step, it increases their effectiveness. 

Example: Navigation systems have one goal: to recommend the fastest route to your destination. The AI model considers various routes and using the condition-action rule, it recommends the fastest route to your destination.

4. Utility-based agents

Utility-based agents choose a sequence of actions that reach a goal while maximizing utility or reward. In simple words, these agents choose the plan with the most rewarding and favorable outcomes. 

‘Utility’ is a metric that is calculated using a utility function. This function measures the usefulness of an action, to each scenario based on a set of fixed criteria. The criteria can include various factors such as progression toward the goal, time requirements, or computational complexity. 

Based on different factors, the selects the plan that maximizes the expected utility. Thus, utility-based agents are useful in cases where multiple scenarios can help achieve one goal but the most optimal plan has to be chosen.

Example: Similar to the last example, a navigation system recommends a route that optimizes fuel efficiently, minimizes the trip time and the toll costs. The agent measures utility to recommend the most favourable route.

5. Learning agents

Learning agents have the same capabilities as other AI agents but are unique due to their ability to learn. In these agents, new experiences are added autonomously to their initial knowledge base. In their reasoning, learning agents may be utility-based or goal-based and are comprised of four main elements:

  • Learning: This element improves the agent’s knowledge base by learning from the environment through its precepts and sensors.
  • Critic: The “critic” element provides feedback to the agent on the quality of its answers based on the required performance standard
  • Performance: This element selects a sequence of actions to be taken upon learning.
  • Problem generator: This element creates various proposals for actions to be taken.

Example: Personalized recommendations on eCommerce sites are the best example here. The agent tracks user activity and preferences and recommends similar products.

Benefits of AI Agents

3D Decorative Representation of Benefits of AI Agents

AI agents can help increase task efficiency, especially on repetitive tasks. But more than that, they are capable of completing tasks accurately. Here are 4 advantages of using AI agents:

1. Task Automation

Recently, there has been a growing interest in workflow automation using AI, or simple said — intelligent automation. The agents can automate complex tasks that may require human intervention otherwise.

Humans do not need to provide any direction to the AI agent for creating and navigating through its tasks. Thus, it allows tasks to be completed at a rapid scale.

2. Quality Responses

Unlike traditional AI models, AI agents are capable of providing responses that are more comprehensive, accurate and personalized. This allows all the tasks and responses to customers to be substantially enhanced. 

This feature is heavily based on adding new information to the memory using external tools. So, the responses improve on their own and aren’t pre-programmed.

3. Greater Performance

Multi-agent frameworks tend to outperform singular AI agents. It happens because the more plans of action are available to an agent, the higher performance it can show. 

Thus, an agent taking knowledge and feedback from other agents specializing in related fields can help in information synthesis. It’s similar to a backend collaboration of AI agents. However, the ability to complete the information depends on the uniqueness of agentic frameworks.

4. Reduced Errors

AI agents aren’t prone to errors and inefficiencies. Thus, they can prevent many potential errors in a process if it’d been done by a human. Since autonomous agents can adapt to changing environments, you can confidently perform complex tasks using them.

Limitations of AI Agents

While artificial intelligence agents offer numerous high-end benefits, they have limitations too — like any other new technology. Here are 4 limitations that make them not so easy to work with:

1. Multi-Agent Dependency

Certain complex tasks require the capabilities of multiple agents. But, there’s always a risk of malfunction when implementing such multi-agent frameworks. The multi-agent systems built on the same foundational models may also have shared pitfalls.

These pitfalls can cause a system-wide failure of all involved agents or expose vulnerability to adverse attacks. Thus, posing a treat for confidential data. This also highlights the importance of data governance in foundation models along with thorough training and testing processes.

2. Infinite Feedback Loops

The convenience of AI agents comes with its own limitations for its users. Agents that cannot create a comprehensive plan or reflect on their findings by themselves end up repeatedly calling the same tools. Thus, creating an infinite feedback loop which requires real-time human monitoring.

3. Computational Complexity

To be real, building an intelligent agents from scratch can be both time-consuming and very computationally expensive. In addition, the resources required to train a high-performance agent can be extensive. Thus, agents may take up to several days to complete a task. 

In addition to these factors, AI agents can impose potential privacy concerns for organizations. Since developing advanced agents requires acquiring and storing massive amounts of data, businesses must be aware of data privacy requirements.

Real-Life Applications of AI Agents

3D Representation of Real-life Applications of AI Agents

AI agents are widely used to enhance customer experience. From personalized suggestions on eCommerce platforms to management, they can be trained to perform any task. 

Here are some real-life applications of Artificial Intelligence agents in various industries:

Healthcare Industry

In the healthcare industry, AI agents can be used for patient service, curate personalized treatment plans, and to overview your provider network. Based on the requirement, these agents can be programmed to improve the workflow. 

For instance, a patient service agent answers questions and helps patients schedule the best physician for themselves.The agent can review a patient’s coverage benefits and generate medical history summaries.

Similarly, these agents can manage patient records, match eligible candidates for clinical trials — all by simplifying patient details and criteria. They can also improve patient wait times.

Finance Industry

Customers expect a high-level of personalization in the finance industry, which may be impossible without AI agents. The agents can draw relevant insights from unified customer data. Thus, assisting humans to create financial recommendations based on each customer’s goals and needs. 

Additionally, advanced agents can help you prepare for client meetings with a detailed review. They can also summarize open cases, orders, generate invoices, and monitor recent activity — all without any human intervention.

Automotive Industry

In the automotive industry, AI agents can be used to review a vehicle’s complete view and its fleet performance. The agent can surface important or time-sensitive vehicle alerts based on the vehicle telematics. 

The AI can also help with maintenance recommendations to quickly resolve issues. Dealerships and repair shops can use AI for promotional events.

Manufacturing Industry

AI agents can monitor machinery to identify maintenance requirements and optimize manufacturing processes. This helps boost productivity and avoid expensive downtime. Moreover, they can boost the sales process.

Examples of AI Agents

Apart from OpenAI’s ChatGPT and Google’s Gemini, there are many other AI agents out there for various purposes. Some of the most common ones are general-purpose AI and agents for developers.

1. General-Purpose Agents

General-purpose agents are only there to fulfill goals and complete tasks. They cannot handle complex tasks. Here are two examples of general-purpose agents:

  • AI Agent is an intuitive app that can help you create your own AI agents. To do so, you have to pick a name, objective, and the AI model to be used (GPT 3.5 Turbo and GPT 4 are available).
  • AgentGPT lets you create and manage multiple AI agents. While it’s intuitive and fast, the results aren’t consistently reliable.

2. AI Agents for Online Research

There are also AI agents to help you with research papers. These can crawl through thousands of pages online to find information with references.
  • aomni is an AI agent that can provide information on any topic you enter. It takes your goal, creates a task list, and hands you over the end-result via email.
  • Toliman AI is another option that follows a similar process. You can select any number of references to put together the end result.

3. AI Agents for Developers

If you know how to code, you can do a lot with AI agents. Some of the tools include GitHub, LangChain, Pinecone, and AgentGPT.

Conclusion

Certainly, AI agents will transform the way businesses process in the future. If they grow at the same pace as general AIs, commercial AI agent platforms may become available in the market. However, that possibility still isn’t so high.

There’s a possibility for artificial general intelligence (AGI) in the future, though. While still unclear, one thing is very clear: this technology is improving critically and is expected to deliver exceptional results.

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