You must be familiar with “AI chatbots”. You give a command — whether to write content, schedule meetings, or analyze data — and it completes the request.
That’s impressive on its own, yes. But also potentially problematic.
The issue? Such AI chatbots rely on human commands. To use AI at a large scale, say to automate business processes, you’ll also need continuous human input on the side.
But, what if the AI bot could make decisions on its own, without the “human command”? That opens up a number of possibilities for productive AI usage.
And that’s exactly what an AI agent does. They are a step ahead of the usual “AI chatbots” and have several real-life use cases.
In this guide, we’ll discuss all about AI agents, how they work, what their benefits are, and how you can implement them in your business. Whether you’re new to AI agents or want to learn more about them, this guide is for you.
What are AI agents?
AI agents are smart systems that can make and execute decisions on their own, with minimal human input. They use artificial intelligence technologies such as natural language processing (NLP) with deep learning to assess situations and execute the best course of action.
Think of them as your digital employees. You initially train them, feed data, and set different outcomes. But once they are configured, AI agents become basically autonomous — asking the right questions, going through the data, and making independent decisions.
That also means — they learn on the go. As the AI agent “works” more and gets “experienced,” it also learns to adapt to new scenarios, drawing from its past experiences and customer responses.1
Chatsonic is a great example for an AI agent that’s designed for marketing use cases. Watch this quick Chatsonic demo to get an idea of how an AI agent works.
Another good example is the new AI agent launched by DeepMind, Project Mariner. It is an in-bowser AI agent that’s designed to automate web search tasks and is powered by Gemini 2.0.
Due to these capabilities, AI agents have been adopted in several business processes, ranging from customer service to marketing. In fact, 25% of enterprises are expected to deploy AI agents by 2025. And the number is expected to grow to 50% in the next two years.
When we say AI agents, we aren’t referring to the AI chatbots that we discussed earlier. While both work on artificial intelligence, there are some fundamental differences between the two.
AI Agents vs. AI Chatbots: What’s the Difference?
AI chatbots are platforms that use artificial intelligence to provide information, answer questions, and solve customer queries. They work on direct commands and don’t have decision-making capabilities.
AI agents, on the other hand, are programmed to make and execute decisions. They are capable of assessing situations, understanding complex queries, and taking action on their own. Depending on the use case, some AI agents may use AI chatbots or large language models (LLM) as their base technology.
In a way, AI agents are the next step to AI chatbots in the artificial intelligence pathway. As Sir Demis Hassabis, CEO of DeepMind, said at The Times Tech Summit:
“I’m sure you’ve used the various state-of-the-art chatbots today. They’re very passive systems…pretty useful for answering a question…what we want next is more agent-based systems that are able to achieve certain goals or tasks that you give it.”
Say you ask it to book a flight. It can provide information on how to book a flight, suggest airlines, and even give you tips on finding the best deals. But, it cannot actually go through the booking process, select flights, and confirm bookings.
However, a travel agency customer service AI agent can do all that. It can understand your preferences, search for flights, compare options, give you the best flight options, and confirm your booking once you make the payment — all without your intervention. This makes AI agents more capable of handling tasks end-to-end.
But we’ve had customer service bots and voice assistants for a long time now. Much before AI even became mainstream. So what’s different about these agents? To understand that, first, you need to understand how they work.
How do AI Agents Work?
“A.I. is made by humans, intended to behave by humans and, ultimately, to impact humans lives and human society.” —Fei-Fei Li, CEO of World Labs, at the New Work Summit.
The way AI agents work is quite similar to how we humans make decisions. We first perceive information from our environment, use information we already know, think about what to do, and then take an action we think is best. All throughout the process, we continue learning from our experience.
Similarly, an AI agent works in four steps:
- Perception: It collects data and perceives the situation using sensors if connected to a physical device (smart home devices, self-driving cars) or through previous training if it’s just a digital agent (customer service bots, voice assistants).
- Thought and decision-making: It interprets data, uses problem-solving techniques, checks potential outcomes, uses various algorithms and analyses, and ends up with a decision.
- Action: It executes the decision. This can be adjusting the thermostat, pulling the car to a stop, or conveying information to a customer.
- Learning: It keeps learning from experience, feedback, or new data and gets better as it is used. When faced with similar scenarios, it can make stronger decisions using its new learnings and past experiences.
Imagine a usual, non-AI customer service chatbot. It can give you automated replies. If you ask it for store hours, it will give a pre-programmed response. But if you ask it which product version is the best for your requirements, you’ll be met with a “Sorry, I can’t help you with that.”
An AI customer service agent, on the other hand, will assess your requirements, give a product recommendation, and help you proceed with the purchase.
This is basically how any AI agent functions. But, the actual technology behind these steps is much more technical and involves several architectures and reasoning models.
The Key Components of AI Agent Architecture
AI agents rely on several essential components to function effectively. This is called the architecture and is crucial to how they work. Here’s a breakdown of these components:
Large Language Models (LLMs)
Large language models (LLMs) are the components that make AI agents “interactive.” If you’re able to put in queries and understand the solutions given by the agent, that’s thanks to LLMs.
These models, such as OpenAI’s GPT or Google’s Bard, form the foundation of many AI agents. They are trained on massive datasets, which results in better, more human-like output.2
Tools Integration
AI agents often integrate with external tools and APIs to perform tasks. This is what gives them a way to execute actions, which we don’t see in AI chatbots.
For instance, a customer service AI agent might connect to a company’s CRM system to fetch user details, check order statuses, or process refunds. Similarly, a marketing AI agent like Chatsonic could link to SEO tools to conduct keyword research.
Memory systems
Now, if you want to “converse” with AI agents, they need some way to recall information and carry the conversation forward or offer personalized replies. That’s taken care of by their “memory.”
There are basically four types of memories AI agents can hold:
- Short-term memory: Remembers only what is said in a single conversation.
- Long-term memory: Remembers everything from past conversations to the present ones.
- Episodic memory: Remembers certain events and information, but doesn’t recall everything.
- Semantic memory: Only remembers general information available in its database.
When you ask a query or give information, the tools are intelligent enough to assess the importance of the data and then decide which memory to store it in.3
Agent Program
This is the core software that brings together all the components. It governs how the AI agent works, i.e. perceives data, reasons, and acts. Usually, the agent program is designed for specific use cases, such as customer support, marketing, data analysis, and so on.
These form the components of the AI agent’s architecture. However the “brain” of the agent, that is its reasoning, can differ in two ways.
AI Agent Reasoning Paradigms
Another important aspect of how AI agents work is how they reason and think. These agents employ specific reasoning methods to make decisions and solve problems. Let’s explore the most common ones:
ReAct
ReAct (Reasoning and Action) is a paradigm where the AI agent combines reasoning with actions in real time. It involves analyzing the situation, thinking about the next steps, and acting based on the analysis.4
These agents are usually programmed to think after every step. That means, that once it perceives a situation, it takes a specific action. Once the action is implemented, it observes how the step affected the situation, thinks again, and takes the next action.
ReWOO
ReWOO (Reasoning WithOut Observation) is a paradigm where the AI agent thinks of the entire plan upfront. Unlike ReAct, ReWOO agents don’t wait for the outcome of their initial actions to decide on the next one.
Instead, they “think” of all possible outcomes, choose the best action course depending on their internal knowledge and previous feedback, and provide an action plan. This is a great paradigm if you want to avoid unnecessary tool usage and also confirm the entire plan before execution.
Most AI agents use these two reasoning paradigms to decide on a course of action, reducing human intervention to a great extent.
Depending on the type of architecture, reasoning, and use cases, AI agents can be classified into multiple types.
The 7 Types of AI Agents
These agents are mainly classified depending on how complex their capabilities are. Here are the main types of AI agents you should know about:
Simple reflex agents
These are the simplest forms of AI agents to exist. They work on a fixed set of rules, perform only preprogrammed functions and use already available information from their database.
These types of agents can’t be trained and they also don’t have any conversation memory. That means you can’t give follow-up commands or queries.
Example: Automatic door sensors. If they detect motion, they open the door. If not, the door remains closed.
Model-Based Reflex Agents
These agents go a step further by having a basic understanding of how the world works. They can predict how their actions will affect the environment and make better decisions.
They have limited short memory but still are bound by a set of rules.5
Example: A GPS-based map that adjusts the route to show the fastest one, based on how much traffic is on each route.
Goal-Based Agents
These agents don’t just react—they aim to achieve specific goals. They think ahead and plan the best steps to reach their objective.
Example: A self-driving car calculates how to safely navigate traffic to reach its destination.
Utility-Based Agents
These agents are a step ahead of the goal-based ones. Utility-based agents aim to not just reach a goal but to do it in the best possible way. They evaluate different options and pick the one that gives the most benefit or satisfaction.
Example: The same self-driving car. But now, it chooses the best speed for optimal fuel efficiency, along with navigating traffic.
Learning Agents
These agents learn and improve over time. Even though they have preprogrammed rules, they get better by observing, trying, and learning from their experiences. They also have conversation memory to replicate results during future interactions.
Example: Generative AI agents become better at answering questions the more they interact with people.
Hierarchical Agents
Hierarchical agents represent a more complex and structured approach. These agents are organized in multiple levels, with higher-level agents managing the tasks of lower-level agents. They perform similar tasks and aren’t independent.
Example: An AI manufacturing agent in an automotive plant. The lower-level agents handle smaller manufacturing tasks while the higher-level agents oversee the manufacturing process as a whole.
Multi-Agent Systems (MAS)
Sometimes, multiple agents work together to achieve a common goal. These are complex systems that “communicate” and “collaborate” with each other to complete the task at hand.6
Example: A smart home system where lights, thermostats, and security cameras work together to make your home comfortable and safe.
Read our detailed guide to learn more about the types of AI agents.
Benefits of Using AI Agents for Your Business
Think of an AI agent as your “ideal” teammate. It can handle manual tasks plus it’s highly productive, always concentrates on the task at hand, and is available all the time. Sounds superhuman? It’s quite like that.
Here’s a full list of how AI agents can benefit you:
Improves productivity and efficiency
If you’ve understood AI agents, you know that they’re capable of continuously handling tasks. But that’s not the only way they make your team efficient.
AI agents also don’t need manual intervention, most of the time. That means, your human teammates can focus on tasks that actually need their attention, while the AI agent handles the monotonous ones.
Enhanced customer experience
AI agents make interactions feel personal by understanding what each customer likes and needs.7 They’re always available—day or night—to answer questions and offer tailored recommendations, which can boost customer engagement and loyalty.
They’re also proactive, spotting potential problems before they happen and fixing them to keep things running smoothly.
Consistency
One of the best things about AI agents? They don’t mess up or forget. Your customers will always get accurate, reliable results, no matter how many times they ask questions.
Scalability
Running a new product launch or an end-of-the-year sale? Regardless of how high the support question volumes are, you can prepare to accommodate them with AI agents. They are highly scalable and can process multiple queries at once, depending on the bot’s bandwidth.
And since AI agents already process basic queries and tasks, there are fewer queries for your human agents to handle.
Cost-Effectiveness
By automating routine tasks and processes, AI agents can help reduce operational costs. They require minimal human intervention, which means you can allocate your workforce to more strategic and productive activities.
Wondering why you should use AI agents? Learn more about the benefits of AI agents for your business.
Due to these benefits, businesses have been implementing AI agents into various processes. Check out some of these real-life use cases of AI agents.
Applications of AI Agents in Real Life
“You can build a very rich agentic world defined by this tapestry of AI agents that can act on our behalf across our work in life, across teams, business processes, as well as organizations.” —Satya Nadella at Microsoft Ignite, 2024.
AI agents can be used in any business process where data analysis, problem-solving, and human input are required. A single AI agent can handle all these steps, and also implement the final action plan, creating a seamless and efficient workflow.
Here are some common AI agent use cases in businesses:
Customer Support AI Agents
AI agents handle customer queries, provide troubleshooting steps, and even process refunds or order replacements without human intervention.
Example: A SaaS company support AI agent may diagnose issues with the software on the customer’s end and provide troubleshooting solutions.
E-commerce AI Agents
These AI agents analyze user behavior and create personalized campaigns for e-commerce businesses.
Example: An e-commerce AI agent can send customized discounts based on a user’s browsing history and purchase patterns.
Healthcare AI Agents
AI agents in healthcare assist in patient care by monitoring health data and alerting doctors about abnormalities.
Example: A wearable AI agent in fitness bands can track heart rates and notify users or healthcare providers if it detects irregularities.
Smart Home AI Agents
These AI agents manage connected devices, ensuring optimal energy usage and convenience.
Example: A smart home AI agent can adjust the thermostat based on your schedule or turn off lights if there’s no one in the room.
Supply Chain Management AI agents
Supply chain management AI agents optimize inventory levels and predict demand trends.
Example: An AI agent for a retailer might forecast which products will sell most during the holiday season and make sure they have sufficient inventory.
Content Marketing AI Agents
Content marketing AI agents suggest keywords, create content strategies, and write content that’s optimized for search engines.
Example: Marketing AI agents can help with SEO keyword research and content planning.
Chatsonic, a marketing AI agent by Writesonic, is one such tool that can assist you with content marketing.
Want to discover more AI agent applications? Check out our article to find 20+ AI agent use cases for your business.
Challenges and Limitations of Using AI Agents
By now, you know what AI agents are, how they work, and their applications. And while AI agents offer numerous benefits, it is also good to be aware of the challenges and limitations that you may face when implementing them:
Ethical Concerns and Bias
AI agents can inadvertently perpetuate biases present in their training data. This can lead to unfair or discriminatory outcomes, especially in sensitive areas like hiring or loan approvals. Usually, it is quite necessary but difficult to ensure unbiased and fair responses in topics related to Your Money or Your Life (YMYL) when using AI agents.
Data Privacy and Security
AI agents often require access to large amounts of data to function effectively. This raises concerns about data privacy and security, especially when handling sensitive customer information. Businesses must ensure robust data protection measures and comply with privacy regulations like GDPR.
Lack of Contextual Understanding
Despite advancements, AI agents can still struggle with nuanced context and complex scenarios. This limitation can lead to misinterpretations or inappropriate responses, particularly in customer-facing roles.
Dependence on Quality Data
The effectiveness of AI agents heavily relies on the quality and quantity of data they’re trained on. Obtaining high-quality, diverse, and unbiased data sets can be challenging and expensive.
Lack of Emotional Intelligence
AI agents, while efficient, often lack the emotional intelligence and empathy that human employees possess. This can be a limitation in roles that require a high degree of emotional understanding or complex interpersonal skills.
Best Practices and Ethical Considerations of AI Agents
As AI agents grow in capabilities and adoption, their ethical use and proper implementation have become critical. Below, we discuss key considerations and best practices to ensure responsible AI deployment:
1. Addressing Bias and Fairness
AI agents can inadvertently perpetuate biases present in their training data, leading to unfair outcomes. For example, recruitment AI agents may favor certain demographics if trained on biased hiring data. In fact, one of Amaqzon’s AI recruiting agents was found to be biased against women.
Even though AI in itself doesn’t have “emotions” and “judgments,” it’s not capable of bias on its own. However, it is trained with human data. And when even subtle biases are present in that data, AI agents tend to amplify it.
To minimize these, conduct regular audits of training datasets and outcomes to identify and mitigate biases. Ensure diverse, high-quality data during development and implement new technologies like retrieval-augmented generation (RAG) to keep AI agents fair.
2. Ensuring Data Privacy and Security
AI agents often process sensitive user information, making them susceptible to privacy breaches. Mishandling data can lead to legal and reputational risks.
To ensure data protection and privacy, implement robust data encryption, comply with regulations like GDPR or CCPA, and adopt practices like anonymizing data to safeguard user privacy.
3. Enhancing Transparency and Accountability
If you’re using AI agents, it’s important to let your employees, customers, users, or any other stakeholders know about AI usage. Users must understand how AI agents make decisions, especially in critical areas like finance or healthcare. A lack of transparency can reduce trust and impact customer relationships.
You can build transparency by providing clear documentation of how you use AI and how it makes decisions. Also, if you’re using customer data to train the AI agents, it’s crucial to specify that, too.
4. Managing Dependency and Failures
Over-reliance on AI agents without proper oversight can lead to operational disruptions if the system fails or makes errors.
To avoid this, establish fallback mechanisms and keep human oversight as a safety net for high-stakes tasks. Continuously test and update AI agents to improve reliability.
5. Promoting Ethical Usage Across Applications
AI agents can be misused by anyone. Say an employee has access to an AI agent, they can use it for automating harmful activities or creating manipulative content. Responsible implementation is crucial to prevent such outcomes.
It’s also essential that you develop ethical guidelines for your AI applications. Additonally, regularly review use cases to ensure they align with societal and organizational values.
How AI Marketing Agent Chatsonic Can Help
Chatsonic is one of the best marketing AI agents that you can choose for your business. From writing content to conducting keyword research and conducting competitive analysis, using the AI agent is like having a marketing expert at your fingertips.
The AI agent relies on multiple LLMs and has well-known SEO and content-writing tools integrated into its workflow. With one single, conversational command, you can build and execute entire content strategies for your website — all within a few minutes.
Want to build an efficient marketing strategy with AI agents? Try Chatsonic today!
FAQs on What are AI Agents
1. Why are AI agents important?
AI agents automate repetitive tasks, enhance efficiency, and solve complex problems, all with minimum human input. They are quite useful in industries like healthcare (diagnosing diseases), education (personalized learning), and transportation (autonomous driving). By reducing human effort and improving decision-making, AI agents allow humans to focus on tasks that actually demand their attention.
2. Is ChatGPT an AI agent?
No, ChatGPT is not an AI agent. Rather, it is an AI chatbot. While ChatGPT has diverse capabilities which include searching the web and answering queries, it requires “prompts” or “human commands” to carry out any task. It is not capable of independent decision-making, which is the defining factor of an AI agent.
3. Is Alexa an AI agent?
No, Alexa is not yet an AI agent. Though it has some AI capabilities such as voice recognition, it cannot perform tasks independently. However, Amazon does plan to deploy Alexa as an AI agent by rehauling its foundation model and including new AI technologies.
4. What are the 7 types of AI agents?
The 7 types of AI agents are simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, learning agents, hierarchical agents, and multi-agent systems (MAS).
5. What is an AI agent in real-life examples?
An AI agent in real-life example is Chatsonic, an AI marketing agent that’s used by marketers to create content, research keywords, optimize articles, and publish them on various platforms. Another example is Project Mariner, an AI agent by DeepMind that automates web browsing tasks.
References
- “Reinforcement Learning: An Introduction” by Richard S. Sutton and Andrew G. Barto ↩︎
- Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P. & Amodei, D. (2020). “Language models are few-shot learners” ↩︎
- Kim, T., Cochez, M., François-Lavet, V., Neerincx, M., & Vossen, P. (2023). “A Machine with Short-Term, Episodic, and Semantic Memory Systems”
↩︎ - Shunyu Yao, Student Researcher, and Yuan Cao, Research Scientist, Google Research, Brain Team. “ReAct: Synergizing Reasoning and Acting in Language Models” ↩︎
- Stone, P., & Veloso, M. (2000). “Multiagent systems: A survey from a machine learning perspective” ↩︎
- Schmid, S., Schraudner, D., & Harth, A. (2021). “Performance comparison of simple reflex agents using stigmergy with model-based agents in self-organizing transportation” ↩︎
- Mohannad A. M. Abu Daqar, Ahmad K. A. Smoudy (2019). “The Role of Artificial Intelligence on Enhancing Customer Experience” ↩︎
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