What are AI agents?
But how is an AI agent defined? An AI agent refers to a system or software that executes tasks independently for a user or another system by structuring its processes and applying the necessary tools. Apart from natural language processing, there are other areas in which agents can be deployed such as decision making, problem solving, interaction with the environment, and task completion.
Autonomous AI agents are applied in different fields in order to solve challenging issues encountered in enterprises, whether in software development, IT automation, code generation platforms, or conversational assistants.
With the power of large language models' ability to process natural language, AI agents can interpret the input given by users and provide relevant outputs as well as determining when to use other external tools.
How AI agents work?
Traditional LLMs only respond according to the data on which they have been trained. However, despite the fact that the foundation of agentic technologies lies in LLMs, their functionality is improved using tool calling on the back end, whereby they can access real-time data, improve workflows, and independently divide complex jobs into smaller ones. Autonomous agents continually adjust according to the user's expectations, use their past experiences, and plan future activities for better responses.
This automated tool use process operates without human involvement, expanding the range of real-world applications for AI systems. The approach AI agents use to accomplish user-defined goals consists of three key stages:
- Goal Setting and Planning
The AI agents can operate independently of any external influences. However, in order to perform any functions, the agents will have to depend on humans as they will set certain goals and define an environment for them. The major three elements that influence the behavior of the agent are:
- A development team that designs and trains AI.
- A deployment team that incorporates an AI agent and makes it accessible for users.
- An end-user who defines goals and provides particular tools for completing tasks.
According to the defined goals and available tools, the AI agent decomposes the principal goal into minor parts, making a plan aimed at achieving it. This is not required when performing simple tasks; instead, the agent should improve its answers step by step.
- Making decisions with limited means
Decisions made by AI agents depend on the information available to them. Nevertheless, they may be unable to provide a comprehensive knowledge base needed for the performance of all subtasks in relation to a given goal. Therefore, they can rely on external sources of information including datasets, web search, API calls, and other AI agents. With the help of acquired data, the knowledge base gets updated regularly, helping to adjust the course of actions.
Consider an example when the user wants to embark on a cross-country road trip and requests the agent to find out the most fuel-efficient route considering traffic and fuel prices. Due to the absence of such functionality in the agent's main algorithmic model, the data is requested from an external source including current fuel prices at nearby gas stations.
In spite of all this new data, the agent cannot decide on the best way that will help save money. The next step that the agent takes is developing another sub-task – communicating with a special navigation AI program that can provide information about real-life traffic and road conditions. As a result of such communication, the agent obtains new information concerning how to choose roads with minimal traffic and less toll roads to reduce fuel expenses even further.
Having obtained the data from different sources, the AI agent starts to analyze and synthesize the data to find trends. Then, it determines the best way that saves money and reduces traveling time. Thus, AI agents are much more versatile than regular AI applications.
- Learning and continual improvement
As part of learning, AI agents improve their responses based on the feedback received. The type of feedback includes that from other AI agents as well as humans involved in human-in-the-loop or HITL. In relation to the cross-country road trip scenario provided, once the response is given, it is stored together with user feedback and new information gained for future improvements.
Where several AI agents have played an integral role in the attainment of the goal, they can also give feedback in order to limit human interventions. In addition, feedback may be given by users during the processes undertaken by the agent to align responses to their goals.
Agentic vs non-agentic AI chatbots
AI chatbots rely on conversational AI techniques, including natural language processing (NLP), to interpret user queries and generate responses. While chatbots serve as a tool for communication, agency refers to the underlying technological framework that provides more advanced functionality.
Non-agentic AI chatbots operate without tools, memory, or reasoning capabilities. They can handle only short-term tasks and lack the ability to strategize or anticipate future steps. These chatbots require continuous user input to generate responses and are effective at handling common queries but struggle with user-specific questions and unique data. Because they do not retain memory, they are unable to learn from past mistakes or refine their outputs based on prior interactions.
In contrast, agentic AI chatbots continuously adapt to user preferences, offering a more dynamic and personalized experience. These advanced chatbots can manage complex workflows by breaking tasks into subtasks, making adjustments autonomously, and refining plans as needed. Unlike their non-agentic counterparts, agentic chatbots can analyze available tools and bridge knowledge gaps with the help of external resources.
ReAct (reasoning and action)
It enables AI agents to think and strategize on what tool should be used in every subsequent step or action. The thinking pattern follows a process called Think-Act-Observe through which agents solve problems and become better with their responses.
By structuring the prompt process, agents get an opportunity to demonstrate how they reason and arrive at their conclusions. In other words, this reasoning pattern is similar to Chain-of-Thought prompting.
ReWOO (reasoning without observation)
In contrast to ReAct, ReWOO does not require agents to depend on the tool's output when making decisions. The agent will come up with a plan on which tools need to be used prior to taking any action.
The ability of an agent to predict its actions ahead of time gives the user an opportunity to assess the effectiveness of the suggested strategy.
Anticipating actions ahead of time enables ReWOO agents to avoid unnecessary repetition of tools and, therefore, reduce the number of tokens used.
Types of AI agents
The levels of AI agents' complexity can vary depending on how they are used. For instance, in cases of low complexity in tasks, a less complex AI agent may prove advantageous. There are basically five different types of AI agents, starting from the least complex agents to the highest levels of complex agents.
Simple Reflex Agents
In these types of AI agents, decisions are purely based on the environmental percept which triggers predefined rules on what action should be taken at particular situations. These agents do not have any memories; rather, they make decisions without considering any source of information externally since all information is accessible within the environment itself.
Model-Based Reflex Agents
This is almost like simple reflex agents because their memory enables them to maintain and update some sort of understanding of the environment internally. This memory makes them work in dynamic and partially observable environment contrary to Simple reflex agents which need all necessary information to be available internally.
Goal-Based Agents
Here, actions taken by the agent depend on particular objectives. These objectives are used by agents to compare different action sequences so as to identify what will give them the greatest benefit.
Utility-based agents
Besides pursuing goals, utility-based agents also seek to maximize a certain value called utility. Through the use of a utility function, they evaluate alternative states according to specific criteria, like efficiency, cost, or time.
Learning agents
They have the ability to evolve because of experience gained from previous interactions. The feedback is used to adjust the agent's knowledge. Usually, four main components are included in the structure of learning agents:
- Learning mechanism – modifies the agent's knowledge through observations.
- Critic – compares performance achieved with anticipated performance.
- Performance element – decides on the optimal action to take based on learning results.
- Problem generator – proposes ways to achieve better performance.
Applications of AI agents
Customer support automation
Many companies use AI-powered chatbots to handle customer inquiries. For instance, ChatGPT-powered virtual assistants on e-commerce websites can answer FAQs, track orders, and provide personalized shopping recommendations, reducing the need for human intervention.
Healthcare assistance
IBM Watson Health helps doctors analyze patient records, suggest possible diagnoses, and recommend treatment plans based on vast medical knowledge, improving healthcare decision-making.
Autonomous vehicles
Tesla’s Autopilot uses AI agents to analyze real-time data from cameras, sensors, and GPS to make driving decisions, such as lane changes, braking, and navigation, enhancing road safety.
Financial trading
AI-powered trading bots like those used by Goldman Sachs analyze stock market trends, predict price movements, and execute trades at optimal moments without human intervention.
Cybersecurity threat detection
Darktrace deploys AI agents to monitor networks, detect unusual activities, and respond to potential cyber threats in real time, preventing data breaches.
Smart home automation
Amazon Alexa and Google Assistant use AI agents to control smart home devices, adjust lighting and temperature, and provide reminders based on user preferences.
Personalized content recommendations
Netflix and Spotify use AI agents to analyze user behavior and suggest movies, TV shows, or songs tailored to individual preferences, enhancing user engagement.
Supply chain and logistics optimization
DHL and Amazon use AI-powered logistics agents to predict delivery times, optimize warehouse management, and automate inventory restocking, improving efficiency.
AI-powered legal assistants
ROSS Intelligence (before discontinuation) used AI to help lawyers research case law, draft legal documents, and predict case outcomes, speeding up legal processes.
AI in education and tutoring
Duolingo and ScribeSense use AI agents to provide personalized learning experiences, offer language tutoring, and automatically grade assignments with accuracy.
Pros and cons of AI agents
Pros
Task automation
Advancements in generative AI have fuelled interest in automating workflows with intelligent agents. These AI-powered tools can handle tasks that typically require human intervention, allowing objectives to be achieved more quickly, cost-effectively, and at a greater scale. Moreover, AI agents can autonomously navigate tasks without constant user input.
Improved performance
Multi-agent systems tend to outperform single-agent models. The ability to integrate insights and feedback from various specialized agents enhances learning and decision-making. Such collaborative approach allows AI agents to synthesize information more effectively, making them highly capable problem solvers.
Higher-quality responses
Compared to traditional AI models, AI agents generate responses that are more precise, and personalized for individual users. The ability to interact with other agents, leverage external tools, and continuously update their knowledge base leads to improvement in reasoning and response and therefore better user experiences.
Cons
Dependence on multiple agents
Some complex tasks require input from multiple AI agents, creating the risk of system-wide failures if one agent encounters issues. When these agents rely on the same foundational models, they may share vulnerabilities, potentially exposing the system to security threats.
Looping feedback cycles
The hands-off nature of AI-driven reasoning comes with challenges. If an AI agent cannot generate a clear plan or adequately reflect on its actions, it may repeatedly invoke the same tools, creating infinite feedback loops.
High computational costs
Developing AI agents from scratch can be both resource-intensive and time-consuming. Training a high-performance agent requires significant computational power, and depending on the complexity of the task, completing certain processes can take days.
Best practices for AI agent deployment
Logs for activity to promote transparency
In cases where multi-agent dependency problems exist, the developer can offer the user a log indicating what the AI agent does. The log should record its interaction with other tools or other agents when making decisions. The transparency will help the user trace the thinking process of the AI agent, spot mistakes, and foster trust.
Interruptibility for more control
Introducing a feature that would enable the user to pause or interrupt the AI agent’s operation is highly suggested whenever there are unintended infinite loops, tools unavailability, and when the agent fails. Users must have such a possibility to stop the AI agents. At the same time, in emergencies, like those requiring quick action, this decision must be made wisely.
Unique identifiers for accountability
The issue of AI agent misuse can be addressed by assigning them a unique identifier. With this approach, it would be easier to find out who develops the agent, who deploys it and who uses it in case any unethical behavior is detected. It could also be important to require authentication when accessing other systems from the agent’s computer code.
Human supervision and approval
It is always useful to have a human giving feedback about AI agent decisions because in some cases, human intervention could be useful in the agent’s learning process. In addition to that, human approval should always be obtained in case the task performed by the AI is related to money transfers or mass communication efforts.
As AI technology keeps growing, the opportunities with AI agents will become even more diverse in the future. If used right, they may offer a strong competitive advantage for any company interested in using them. All you need is to know what an AI agent can and cannot do to benefit your company.
Are you ready to discover the power of AI agents?
Logs for activity to promote transparency
In cases where multi-agent dependency problems exist, the developer can offer the user a log indicating what the AI agent does. The log should record its interaction with other tools or other agents when making decisions. The transparency will help the user trace the thinking process of the AI agent, spot mistakes, and foster trust.
Interruptibility for more control
Introducing a feature that would enable the user to pause or interrupt the AI agent’s operation is highly suggested whenever there are unintended infinite loops, tools unavailability, and when the agent fails. Users must have such a possibility to stop the AI agents. At the same time, in emergencies, like those requiring quick action, this decision must be made wisely.
Unique identifiers for accountability
The issue of AI agent misuse can be addressed by assigning them a unique identifier. With this approach, it would be easier to find out who develops the agent, who deploys it and who uses it in case any unethical behavior is detected. It could also be important to require authentication when accessing other systems from the agent’s computer code.
Human supervision and approval
It is always useful to have a human giving feedback about AI agent decisions because in some cases, human intervention could be useful in the agent’s learning process. In addition to that, human approval should always be obtained in case the task performed by the AI is related to money transfers or mass communication efforts.
As AI technology keeps growing, the opportunities with AI agents will become even more diverse in the future. If used right, they may offer a strong competitive advantage for any company interested in using them. All you need is to know what an AI agent can and cannot do to benefit your company.
Are you ready to discover the power of AI agents?