What are AI Agents?

AI agents come in all kinds of different shapes and sizes, each designed for specific tasks. Some follow fixed rules, while others adapt and learn over time. The main types of AI agents include: 

1.   Reactive agents 

These agents respond to situations without storing past experiences. They follow predefined rules and make decisions based only on current inputs. Chess-playing programs and basic customer service chatbots fall into this category.  

2.   Model-based reflex agents 

These agents use internal models to understand their environment. They track changes and adjust their responses accordingly. A thermostat that regulates temperature based on past readings is an example of a model-based reflex agent.   

3.   Goal-based agents 

These agents evaluate different actions and choose the best one to reach a specific goal. They consider possible outcomes before making decisions. Self-driving cars use this type of agents to navigate safely.   

4.   Utility-based agents 

These agents prioritize efficiency by selecting the most beneficial action. They weigh risks, rewards, and probabilities to improve results. Stock trading bots and recommendation systems operate this way.   

5.   Learning agents 

These agents improve over time by analyzing past experiences. They refine their strategies based on success and failure. Example include virtual assistants, which become more accurate by learning user preferences.   

AI agents rely on several key components to function effectively. Each of these plays a role in how the agentic AI network gathers information, makes decisions, and acts. These components, which work together to help the AI agent respond with precision and speed under different circumstances, include:   

1.   Perception  

Perception components are what allow AI agents to collect information from their surroundings. Digital agents process text, images, and audio, while physical agents use sensors to detect movement, temperature, or other environmental factors. A voice assistant, for example, listens to speech commands, while an autonomous vehicle constantly monitors the road for activity. Without perception, an AI agent would have no way to access the world around it.   

2.   Decision-making 

Decision-making components enable AI agents to analyze information and choose the best response. Some of these components follow simple rules, while others use RAG GenAI models to predict outcomes. A fraud detection system, for instance, examines transaction data and flags suspicious activity in real time. Decision-making is critical for AI agents that must evaluate multiple options before acting.   

3.   Learning ability 

Components with learning ability allow AI agents to improve over time. By analyzing past experiences, they refine their strategies and adapt to new situations. For example, the recommendation system of a streaming service like Netflix or Spotify, learns a user’s preferences and suggests movies or music based on past choices. Without learning capabilities, AI agents would remain static and be unable to handle unfamiliar tasks.

4.   Memory 

Memory components help AI agents store and recall important information. Short-term memory allows them to track ongoing interactions, ensuring they maintain context in a conversation. Long-term memory enables them to apply past lessons to future decisions. An AI customer service agent, for example, remembers previous interactions to provide better assistance. Without memory, AI agents would treat every request as a new one, even from repeat users.   

5.   Action execution

The action execution component enables an AI agent to complete a task. Digital agents may generate reports, send emails, or process transactions, while physical agents perform actions like adjusting machine settings or moving objects. A robotic vacuum, for instance, maps a room and then cleans it based on its plan. Action execution turns AI decisions into real-world results.   

AI agents rely on different architectures to process information, make decisions, and complete tasks. Some follow strict rules, while others adjust to new information and situations. Choosing the right approach depends on the complexity of the task and the need for speed or adaptability. The most common AI agent architectures are deliberative, reactive, and a hybrid combination of the two:

• Deliberative architectures employ careful planning and logical reasoning. Agents using this architecture can analyze data, evaluate different options, and decide on the best course of action. They work well in situations that require structured thinking, such as financial analysis, legal research, and medical diagnosis. A legal AI virtual assistant, for example, can review case law, apply legal rules, and suggest strategies. While deliberative agents are accurate and thorough, they can be slow because they process every decision in detail.   

• Reactive architectures focus on quick responses. These agents do not store past experiences for future reference. Instead, they react instantly to new inputs based on predefined rules. Fraud detection systems use a react agent LLM to flag suspicious transactions the moment they occur. Self-driving cars also rely on reactive agents to avoid obstacles in real time. While this approach ensures fast decision-making, it’s inappropriate for complex tasks requiring long-term analysis.

• Hybrid architectures combine planning and quick reactions. These agents can analyze data, make immediate decisions, and adjust their strategies over time. Autonomous robots, for example, use hybrid architectures to navigate changing environments while following a planned route. Virtual assistants also use this approach, responding instantly to voice commands while taking user preferences into account. Hybrid architectures are more flexible, making them ideal for tasks that require both quick reflexes and long-term reasoning.   

AI agents enhance efficiency across various industries by automating tasks, analyzing data, and making informed decisions. Here are some notable applications: 

1.   Customer service  

Today, customer service chatbots are widely used to provide quick responses to common customer inquiries.

Forward-looking companies, like Pelephone, are deploying chat apps internally to assist human reps in handling time-consuming and error-prone tasks. For example, GenAI Rep Assist can review several customer invoices simultaneously to find discrepancies in seconds.

4-phase GenAI customer service adoption  
inevitably leading to autonomous AI agents 

Source: K2view State of Data Readiness for AI in 2024 report 

Over time, human intervention will be phased out, culminating in LLM-powered autonomous agents trained to proactively act to prevent common calls and even anticipate customer needs based on AI personalization  

2.   Healthcare 

AI agents assist doctors by analyzing medical records, suggesting treatments, and detecting early signs of diseases. Some systems help schedule appointments and monitor patient conditions remotely. IBM's Watson for Oncology, for instance, analyzes medical literature and patient data to recommend personalized cancer treatment plans.¹

3.   Finance 

AI agents track market trends, detect fraudulent transactions, and automate trading strategies. Banks and financial institutions use them to assess risks and recommend investment opportunities. PayPal, for example, uses AI algorithms to analyze customers’ transaction histories, preferences, and behaviors. By gathering insights from these patterns, it can suggest preferred payment methods for seamless and convenient transactions.²

4.   Autonomous vehicles 

AI agents control self-driving cars by processing sensor data, predicting traffic movements, and making split-second driving decisions. These systems help improve road safety and efficiency. For example, Tesla's Autopilot utilizes advanced AI algorithms to analyze sensor data in real-time, allowing it to make informed decisions based on changing road conditions. This advanced technology not only enhances safety but also contributes to the overall advancement of AI in the automotive industry.³

5.   Personal assistants 

Virtual assistants, like Amazon Alexa, Apple Siri, and Google Assistant, use AI agents to help users set reminders, control smart home devices, and retrieve information. For example, Amazon recently announced that Alexa+ – based on a state-of-the-art architecture that automatically connects a variety of Large Language Models (LLMs), agentic capabilities, services, and devices at scale – will now be more conversational, smarter, personalized, and capable of getting more things done.⁴

6.   Cybersecurity 

AI agents identify potential security threats, monitor network activity, and respond to cyberattacks in real time. Organizations use them to protect sensitive data and prevent unauthorized access. Microsoft Defender, for instance, uses an AI agent to automatically identify, evaluate, and prioritize vulnerabilities. Its Vulnerability Management agent continuously monitors newly published threats, assesses their risk levels, and offers clear, actionable recommendations for remediation.⁵

Governments and regulatory bodies are working to establish clear guidelines for the use of AI and AI agents. As AI adoption increases, laws must keep pace to address risks related to privacy, security, and accountability.  

One of the most comprehensive AI regulations is the European Union’s AI Act. It categorizes AI systems based on risk levels, banning high-risk applications while requiring transparency and oversight for others. AI used in healthcare, finance, and law enforcement faces stricter requirements due to its potential impact on human rights and safety.  

In the United States, AI regulation is developing through a mix of federal and state policies. The White House AI Bill of Rights provides ethical principles for AI use, while states like California have passed data privacy laws like CCPA that affect AI-driven decision-making. Regulators focus on preventing bias, ensuring transparency, and protecting consumer rights.  

China has taken a more centralized approach, enforcing strict AI laws that require companies to register AI models and ensure compliance with government standards. AI agents must align with national regulations, and developers must prevent misinformation or harmful use cases.  

Industry groups and global organizations also shape AI policies. The OECD AI Principles emphasize fairness, transparency, and accountability, while the G7’s endorsement of the Hiroshima AI Process encourages international cooperation on AI governance.  

As AI regulations continue evolving, businesses must stay ahead of compliance requirements. Organizations deploying AI agents should monitor legal developments, conduct regular audits, and ensure AI systems align with ethical and legal expectations. 

AI agents provide many benefits, but they also present challenges. As organizations rely more and more on AI agents, they increasingly face issues related to accuracy, security, fairness, and accountability.

In a recent survey of 300 AI experts, we found that, besides cost, the top 3 challenges for GenAI – and AI agents by association – are data-related as highlighted in purple below: (1) Data security and privacy, (2) the reliability of LLM responses (which is completely dependent on grounding the model with fresh structured data), and (3) enterprise data readiness for GenAI.

One major challenge is bias in AI decision-making. AI agents learn from data. So, if the training data contains biases, the agent may produce unfair or discriminatory results. In hiring, for example, AI-powered recruitment tools have sometimes favored certain groups over others because of biased historical data. Ensuring fairness requires careful selection of training data and ongoing monitoring to prevent unintended discrimination.  

Another concern is lack of transparency. Many AI agents operate as "black boxes" in the sense that users can’t easily understand how they make decisions. This creates problems in fields like finance, healthcare, and law, where AI-generated recommendations can have serious consequences. If a medical AI system suggests a treatment, doctors must understand its reasoning to ensure the best outcome. Developing explainable AI models – with increased transparency – can help users trust AI agents and use them responsibly.  

Data privacy and security also pose significant challenges. AI agents process vast amounts of sensitive information, including personal, financial, and medical data. Without strong security measures, these systems can become targets for cyberattacks. Unauthorized access to AI-powered financial platforms, for example, could lead to fraud or identity theft. To avoid this phenomenon, organizations need to implement strict security protocols and comply with data protection regulations to safeguard user information.  

Dependence on AI agents also raises concerns about job displacement. Automating tasks can improve efficiency, but it can also reduce the need for human workers in some industries. Many businesses use AI agents to handle customer support, analyze contracts, or process transactions – replacing jobs traditionally performed by people. While AI agents create new opportunities, companies must find ways to retrain workers and help them transition into roles that still require human judgment and creativity.  

Finally, AI agents often struggle with context and adaptability. While they excel at processing structured tasks, they often misunderstand complex human emotions, cultural differences, or subtle nuances in language. Chatbots, for example, may provide incorrect or insensitive responses because they don’t understand user intent.  

Addressing these challenges is essential for the responsible development and implementation of AI agents. Organizations must balance innovation with ethical responsibility, ensuring that AI agents remain fair, transparent, and secure while complementing human intelligence rather than replacing it. 

As discussed in the previous section, AI agents work best when they support human intelligence rather than replace it. In many industries, AI agents can handle routine or data-heavy tasks, freeing people to focus on strategy, creativity, and complex decision-making. For example: 

• In healthcare, AI can analyze medical scans and flag potential issues, but doctors make the final diagnosis. AI speeds up research by processing vast amounts of medical data, yet human expertise ensures accurate interpretation.  

• In finance, AI detects suspicious transactions within seconds, but human analysts assess broader economic trends and market conditions.  

• In creative industries, writers use AI tools to brainstorm ideas, while designers rely on AI-powered software for faster prototyping. AI agents can speed up workflows, but human oversight ensures originality and relevance.  

• Customer service teams integrate AI chatbots to handle basic inquiries, reducing wait times. But when a situation becomes too complex, the system transfers the case to a human representative.  

• Cybersecurity pros use AI agents to help them detect unusual patterns in network activity. Yet while AI responds quickly to threats, human experts must analyze incidents and make strategic decisions to prevent future attacks.   

As AI becomes more advanced, businesses must design systems that enhance human strengths rather than replace them. A thoughtful approach to generative AI adoption creates smarter workflows, improves decision-making, and allows professionals to focus on high-value tasks. The best results come from a partnership where AI and human intelligence work together.

AI agents continue to evolve, becoming more powerful, adaptable, and intelligent. As technology advances, these agents will play an even greater role in business, healthcare, finance, and everyday life. Several key trends will shape their future and expand their capabilities, notably:   

1.   Autonomy 

LLM-powered autonomous agents are moving beyond simple task execution and developing the ability to make complex decisions with minimal human intervention. In industries like cybersecurity, AI agents will detect and neutralize threats in real time without waiting for human approval. In customer service, they will handle sophisticated interactions, leveraging powerful technology like RAG conversational AI to resolve issues with more up-to-date data and more natural and human-like communication.   

2.   Multimodality 

Multi-agent LLM systems will process and interpret multiple types of data, including text, images, video, and sound. This will improve their ability to understand context and respond accurately. Healthcare AI, for example, will analyze medical images alongside patient records to provide more precise diagnoses. Autonomous vehicles will use AI agents that combine visual, audio, and sensor data to navigate safely in complex environments.   

3.   Personalization 

AI agents will increasingly adapt to individual users, learning preferences and tailoring responses based on past interactions. Virtual assistants will provide more relevant recommendations, while AI-driven education tools will adjust lessons based on a student’s progress. Personalized AI will enhance user experiences across entertainment, shopping, and professional services.  

 4.  Collaboration 

AI agents will also become more collaborative, working alongside humans and other AI systems. Businesses will use multi-agent systems where different AI agents communicate, share data, and solve problems together. This will improve efficiency in areas like supply chain management, where AI agents will coordinate logistics, predict demand, and optimize deliveries in real time.   

2025 will be the year of AI agents 

Many industry analysts and tech execs believe that 2025 will be the year of AI agents, which are becoming more mainstream and impactful. Here’s why:  

• Increased focus and investment 

  AI agent technology is gaining significant traction, with major tech companies like Microsoft and Nvidia actively investing in and developing AI agents.⁶

• Beyond chatbots 

  AI agents are moving beyond simple chatbot interactions and are expected to be able to perform complex, multi-step tasks that require reasoning and problem-solving.⁷ 

• Real-world applications 

  AI agents are expected to find practical applications in various industries, including customer service, supply chain optimization, sales and marketing, IT and security, finance, and human resources.⁸ 

• Examples of AI agents 

  AI agents can be used for tasks like automating data entry, interacting with customers, maintaining inventory counts, and even performing complex tasks like software engineering or cybersecurity.⁹ 

• Industry predictions 

  Nvidia CEO Jensen Huang has stated that "I think this year we're going to see it take off" referring to AI agents.⁹ 

• AI agents as virtual assistants 

  Some believe that AI agents could even function as virtual co-workers, assisting with tasks and workflows.¹⁰

• Focus on Agentic AI 

  The term "agentic AI" is gaining traction, referring to AI systems that can act autonomously to complete tasks, rather than simply answer questions.⁷ 

K2view enhances AI agents  

K2view enhances the effectiveness of AI agents by providing tools that simplify their creation and integration with enterprise data. One such tool is the recently-launched Data Agent Builder, a no-code platform that allows organizations to easily develop intelligent, data-driven AI agents. The K2view platform enables users to build sophisticated agents capable of answering data-related queries in real time, streamlining the development process and reducing the need for manual coding.  

The K2view Data Agent Builder offers features like chain-of-thought prompting, which enables AI agents to reason through complex problems to take the most appropriate actions. Equipped with LLM text-to-SQL capabilities, it’s based on a Retrieval-Augmented Generation (RAG) framework which simplifies access to, and understanding of, data across multiple sources. An advanced RAG architecture lets you create AI agents that can process and interpret complex datasets for more accurate decision-making and responses.