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AI Agents are shaping technological trends, marked by milestones such as the launch of Astra during Google I/O 2023 or the emergence of GPT-4o. Major corporations are investing billions of USD in this technology to automate processes and optimize efficiency. In this article, FPT.AI will guide you in exploring how AI Agents are helping businesses improve workflows, enhance customer experiences, and optimize operational activities.
What are AI Agents? The Fundamental Components of an AI Agent
AI Agents are software or hardware systems designed to perform tasks automatically and independently to achieve specific goals.
An AI Agent can process information, make decisions, and perform actions to interact with related conditions and systems. AI Agents can be applied in various fields, ranging from virtual customer assistants to complex automated control systems, or even robots operating in real-world environments.
Key Components of an AI Agent including:
- Sensors: AI Agents can perceive and collect information from their surroundings. This can be through physical sensors (if the agent is a robot) or software interfaces that gather data from online sources.
- Processors: These serve as the “brain” of the AI Agent, where data is analyzed and processed to make decisions. AI Agents utilize algorithms (such as search algorithms, optimization algorithms, or neural networks and deep learning models) combined with machine learning and artificial intelligence models to analyze information and calculate necessary actions.
- Memory: Memory enables AI Agents to store information for future use, such as the history of decisions made or learned rules. It retains data that the AI Agent needs to complete tasks optimally or to learn and improve performance over time.
- Actuators: AI Agents execute actions based on the decisions they make. For robots, actuators might include components that enable movement or manipulation of objects. For software agents, this could involve sending information or executing commands within a system.
Amazon’s Alexa is a notable example of an AI Agent. Integrated into devices like the Echo smart speaker, Alexa helps users perform various daily tasks via voice commands such as answering questions, setting alarms, playing music or controlling smart home devices namely lights or temperature settings.
Additionally, Alexa assists users in online shopping, placing orders from Amazon, and creating to-do lists. It learns from user interactions to personalize the experience, such as recommending favorite products, songs, or reminders based on daily habit.
Characteristics of an AI Agent
Autonomy
AI Agents can operate without direct human intervention, independently making decisions and executing actions. This capability enables AI Agents to handle complex tasks in real-time without requiring detailed step-by-step programming.
For example, in Tesla’s self-driving car system, AI Agents can adjust speed, change lanes, and stop based on sensor data regarding road conditions, obstacles, and other vehicles. In emergencies, the AI Agent can automatically brake or adjust the route without driver intervention. This demonstrates the high level of autonomy in the system, allowing vehicles to operate safely without human control.
Continuous Learning
AI Agents have the ability to continuously learn and improve by receiving feedback from their environment or external critiques. This feature helps AI not only perform tasks more effectively but also adapt to changes in its surroundings.
An illustration of this characteristic is a customer support chatbot that initially may only answer simple questions. However, through data gathered from customer interactions, it gradually learns to understand more complex queries and improve its responses. For instance, the Messenger chatbot has learned from millions of conversations, enabling it not only to grasp context but also to automatically offer more appropriate solutions to customers.
Reactive and Proactive
AI Agents not only respond to changes in their environment but also possess the ability to predict and take action before events occur. A detailed example of this characteristic can be observed in the Nest Thermostat, a smart temperature control device developed by Google.
The Nest Thermostat not only reacts to immediate temperature changes within a home but also learns from user habits and environmental factors to make predictive adjustments. For instance, after a period of use, the Nest Thermostat learns from user adjustments, noting when indoor temperatures rise due to midday sunlight or when to increase heating on cold mornings.
Moreover, the Nest Thermostat can react swiftly to sudden changes in the environment. If temperatures drop or rise too quickly, the system adjusts immediately to maintain comfort levels and save energy.
How do AI Agents Work?
AI Agents operate through a multi-stage process designed to simplify and automate complex tasks. Initially, the AI Agent receives a specific objective from the user. Based on this objective, the AI Agent plans and breaks it down into smaller, actionable tasks.
During execution, the agent gathers information from various sources, such as transaction data or customer interaction histories. To ensure effectiveness, the AI Agent can access the Internet, databases, or additional tools to retrieve the necessary information. After sufficient data is collected, it analyzes the information and makes decisions using machine learning models to carry out the tasks.
Throughout this process, the AI Agent continuously evaluates and adjusts its actions based on environmental feedback, improving incrementally with each interaction. Its ability to learn and adapt continuously allows the AI Agent to self-optimize, providing better responses over time.
Ultimately, the agent can independently perform complex tasks, such as customer support, handling requests, or providing product recommendations.
Key Differences Between AI Agents and AI Chatbots
Below is a comparison table highlighting the distinctions between AI Agents and AI Chatbots:
| Criteria | AI Agent | AI Chatbot |
| Primary Purpose | Executes automated and independent tasks, often without human interaction | Interacts with humans, primarily for customer support or answering questions |
| Automation Capability | Fully automates tasks without human intervention | Lacks full automation capability, relying on human interaction |
| Human Interaction | May not require human interaction during operation | Mainly interacts with humans via text or voice |
| Form | Can be software, physical robots, or smart home devices (e.g., vacuum robots, smart thermostats) | Primarily exists as text or voice-based interfaces (chat applications, chatbots, or virtual assistants) |
| Task Processing Ability | Capable of handling complex tasks like automation and data-driven decision-making | Limited to basic questions and answers, with less capability for handling complex tasks |
| Application Scope | Broad, applicable in various domains such as automation, medical diagnostics, and personal finance | Primarily in customer support, answering queries, and chat-based interactions |
| Response Mechanism | Proactive and reactive to the environment, capable of planning and complex decision-making | Reacts based on predefined scripts and dialogue models, often limited in contextual understanding |
| Learning Capability | Continuously learns and adapts based on feedback from the environment and other agents | Typically lacks continuous learning ability; updates are manual |
| Context Processing | Deep understanding of context and emotions, capable of processing social signals and complex scenarios | Relies on fixed scripts, struggles with non-standard requests |
| Example Applications | Robotic vacuum cleaners, business automation systems, smart thermostats, personal financial assistants | Customer support chatbots on websites, virtual assistants like Siri or Google Assistant |
Popular Types of AI Agents
There are various types of AI Agents, each suited for specific tasks and applications. Below are some common types of AI Agents:
- Simple Reflex Agents: These operate based on the “condition-action” principle and react immediately to the environment using simple rules.
- Model-Based Reflex Agents: These agents construct an internal model of the surrounding world, enabling them to make predictions and decisions based on ambiguous or incomplete data.
- Goal-Based Agents: These agents make decisions by focusing on achieving specific objectives, taking into account the consequences of their actions to ensure optimized results.
- Utility-Based Agents: By using utility functions, these agents compare different scenarios and choose the optimal course of action based on the most beneficial outcome.
- Learning Agents: These agents learn from previous interactions and experiences, leveraging feedback from the environment or users to improve performance over time.
- Hierarchical Agents: These are a collection of AI Agents organized in a hierarchy, where higher-level agents delegate tasks to lower-level agents to achieve a common goal.
Benefits of Using AI Agents
In today’s context, delivering high-quality customer service is a critical factor for 83% of consumers when considering a purchase. However, 77% of businesses struggle to provide consistent customer experiences across different channels. AI Agents emerge as a crucial solution to this problem, offering the following four key benefits:
- Improved Productivity: AI Agents automate repetitive tasks, freeing up human resources from manual work. This allows businesses to focus on strategic and creative activities, boosting overall organizational productivity.
- Cost Reduction: By optimizing processes and minimizing human errors, AI Agents help businesses cut operational costs. Complex tasks are handled efficiently by AI Agents, which can also adapt to environmental changes without requiring continuous human intervention.
- Informed Decision-Making: AI Agents leverage machine learning (ML) technologies to gather and analyze real-time data, enabling managers to make better, faster, and more accurate decisions. This is especially valuable for strategic planning, such as analyzing product demand or market trends.
- Enhanced Customer Experience: AI Agents personalize experiences for each customer, offering quick and continuous responses 24/7. This boosts customer engagement, improves loyalty, and creates more appealing service solutions.
With these benefits, AI Agents become a powerful tool that not only enhances operational efficiency but also significantly improves customer experiences in today’s highly competitive environment.
>>> Read more about: What is Generative AI? Trends in Applying GenAI from 2024 to 2027
Challenges and Considerations When Using AI Agents
Although AI Agents are opening up groundbreaking potential in areas like automation, programming, and customer support, the technology is still in its infancy and faces significant challenges.
According to Kanjun Qiu, CEO and founder of the AI research startup Imbue, the development of AI Agents today can be compared to the race to develop self-driving cars a decade ago. While AI Agents can perform various tasks, they are not yet reliable enough and cannot operate entirely autonomously. One of the most significant issues AI Agents encounter is limitations in logical reasoning. As Qiu points out, AI programming tools can generate code, but they often write incorrect code or fail to test their outputs independently. This necessitates constant human intervention to refine processes.
Dr. Fan also notes that we have not yet reached a stage where AI Agents can fully automate daily repetitive tasks, as the systems still exhibit tendencies for “nonsense generation” and do not always adhere to user instructions precisely.
Another major limitation is the context window—the ability of AI models to read, understand, and process large volumes of data. Dr. Fan explains that models like ChatGPT can handle programming but struggle with long and complex code segments, whereas humans can easily follow hundreds of lines of code without difficulty. Companies like Google have had to enhance context processing capabilities in their AI models, such as with the Gemini model, to improve performance and accuracy.
For AI Agents with physical forms, like robots or virtual characters in games, training them to perform human-like tasks also poses a challenge. Currently, the data available to train such systems is limited, and research is only beginning to explore how generative AI can be applied to automation.
>>> Explore: What is an LLM Agent? How it works, advantages, and disadvantages
Building AI Agent Teams: A Key Technology Trend for 2025
According to Gartner, AI Agents top the list of strategic technology trends for the coming year. The organization also predicts that by 2028, at least 15% of daily work decisions will be automated by AI Agents. Moreover, Insider reports that the AI Agents market, valued at $3.7 billion in 2023, is expected to reach $103.6 billion by 2032, with a compound annual growth rate (CAGR) of 44.9% from 2024 to 2032.
The value AI Agents bring to businesses lies in their ability to automate a wide range of complex use cases, saving both costs and time. Common tasks supported by AI Agents include software development, automated customer support, business operations, cybersecurity, and risk detection.
Recognizing this trend, many companies have begun building their own AI Agent teams. For example, in 2023, FPT Long Chau partnered with FPT Smart Cloud to establish an AI Agent workforce to support pharmacist training—those who are the front-line employees interacting directly with customers. The training materials for more than 16,000 pharmacists across 2,000 pharmacies were personalized through FPT AI Mentor.
Enhancing pharmacists’ competencies contributed significantly to FPT Long Châu’s business operations. Within the first nine months of the year, the pharmacy chain recorded a revenue growth of 62%, reaching VND 18.006 trillion, accounting for 62% of FRT’s total revenue and completing 85% of its 2024 plan.
AI Agent Market Prospects in Vietnam
According to the global consulting group IMARC, Vietnam’s AI market reached a value of USD 547.1 million in 2023 and is projected to soar to USD 2.06 billion by 2032, reflecting a compound annual growth rate (CAGR) of 15.8%.
Vietnamese tech giants have been aggressively investing in the race to develop AI Agents. Notably, FPT Group announced the launch of two AI factories (FPT AI Factory) in Japan and Vietnam in November. Additionally, FPT Smart Cloud (a subsidiary of FPT Group) introduced FPT AI Agents, a platform for creating and managing multilingual AI Agents in English, Vietnamese, Japanese, and Indonesian.
With this technological advancement, businesses can create their own AI Agents within minutes or hours. These AI Agents stand out with unique personalities and characteristics, as they are developed based on the specific Data and Culture of each organization. This differentiation fosters a completely new competitive edge among enterprises and extends to building AI sovereignty among nations.
Built on the robust infrastructure of FPT AI Factory, FPT AI Agents promise significant breakthroughs in operational efficiency, exceptional customer experiences, and unlimited creative potential for all businesses.
Learn more about the FPT AI Agents platform at: https://fpt.ai/products/fpt-ai-agents-en/
