AI agent vs chatbot: Key differences explained

What is a chatbot?

A traditional chatbot is a computer program that relies on pre-defined rules, decision trees, and scripted responses to interact with users. First created in 1964, chatbots are primarily used to handle basic interactions, retrieve information, and answer common customer support questions. These days, the best chatbots use AI — but they serve basically the same purpose.

An AI chatbot is a software tool designed to simulate human conversation with users through voice or text interactions. It uses conversational AI techniques like natural language processing (NLP) to understand user queries and automate relevant responses to them.

AI chatbots are most effective when trained on a specific dataset and programmed with responses to certain business-critical questions. This enables the chatbot to generate accurate and relevant answers to user queries while enhancing routine interactions with human-like conversation.

But because they use a less advanced form of AI, chatbots cannot understand context beyond their training or make decisions on their own. As a result, they struggle to respond accurately to user inputs that fall outside the scope of their knowledge base, which is typically derived from a large language model (LLM), plus any specific data they’ve been trained on.

AI chatbot use cases

AI chatbots are commonly used to automate customer interactions and streamline business processes. Well-suited for handling high volumes of routine customer service queries and routine interactions, they're a cost-effective solution to customer service automation.

Here are some common AI chatbot use cases:

• Customer support: An ecommerce site could implement an AI chatbot to answer FAQs about its shipping and returns policies, as well as recommend products. The ecommerce AI chatbot interprets the customer queries and generates the most relevant response based on its training, providing immediate answers 24/7 and reducing the workload for customer service reps.

• Automated scheduling: A hospital could deploy an AI chatbot on its app and website to allow patients to book and manage appointments independently. The AI chatbot for healthcare is integrated with the scheduling software, helping to gather patient information and streamline patient intake to increase efficiency.

• Lead generation: A real estate company could launch an AI chatbot on its website that captures and qualifies leads as it answers FAQs and suggests property listings. The real estate AI chatbot gathers contact information, flags qualified leads based on predefined criteria, and integrates lead data to help agents close more deals.

AI chatbot example: IT support

A business could use an AI chatbot as the first point of contact for employee IT issues. Using natural language, the chatbot can ask a sequence of basic questions to identify the issues and troubleshoot common problems like connectivity issues or password resets. If the solutions offered from its knowledge base don’t work, the chatbot will escalate to a human agent.

What is an AI agent?

An AI agent is a more advanced artificial intelligence system that has a degree of autonomy and can act on its own to achieve the goals set for it. Unlike AI chatbots that can only respond to user inputs, AI agents can autonomously make decisions, execute multi-step plans, and even integrate outside data and tools to solve complex problems.

Built on large language models (LLMs), AI agents use sophisticated machine learning and natural language processing to understand and interact with their environment. Unlike simpler systems, AI agents can understand context, learn from interactions, and adapt their strategies over time to a specific goal. Given their autonomy and advanced capabilities, AI agents are capable of handling a broader range of challenging and open-ended tasks.

AI agent use cases

While it's still early days, AI agents promise to handle various tasks that require making decisions, using multiple data sources, and understanding context.

Here are some of the most exciting AI agent use cases:

• Business operations: Capable of collecting and processing vast amounts of data in real time before making decisions, AI agents are set to reshape business operations. They could autonomously manage supply chains, optimize inventory levels, coordinate communications, forecast demand—all while minimizing human input on routine tasks to improve efficiency and reduce costs.

• Cybersecurity: A business could deploy an AI agent to serve as the tireless guardian of network security. The AI agent could continuously monitor network traffic, detect threats and anomalies, and respond in real-time without constant human oversight. Both proactive and adaptive, the AI agent could strengthen the company's defense and ensure a cybersecurity posture that’s more secure, scalable, and cost-effective.

• Software development: A business could deploy an AI agent to manage its entire development lifecycle rather than just generate code. The AI agent could design system architecture, write code, detect bugs, and more to dramatically accelerate the software development process and make it more cost-effective.

AI agent example: IT support

Returning to the prior example of IT support, how would an AI agent handle a support request differently? Unlike a chatbot, the AI agent could resolve a more complex troubleshooting issue all on its own. It would start by breaking down a complex objective into a series of subtasks, then use external tools and knowledge sources to achieve a solution.

For example, the AI agent could:

1. Query the company's database for service history and device logs

2. Request real-time diagnostics on devices and systems via an API (application programming interface) call

3. Analyze the collected data to identify potential causes and suggest solutions

What are the differences between AI chatbots and AI agents?

These two AI technologies are often confused because they both interact with users through natural language. However, AI agents and AI chatbots differ in many important ways, specifically:

Problem-solving capabilities

AI chatbots are designed to handle specific and contained tasks, such as answering FAQs, executing basic transactions, and gathering information. To generate a user response, chatbots use natural language processing to interpret an input and match it to the most appropriate output in their structured knowledge base. But they lack the ability to reason or solve multi-step problems.

AI agents, on the other hand, are capable of autonomous decision-making. By combining goal-oriented behavior and machine learning, AI agents can tackle multi-step objectives in novel ways within the boundaries set for it. If given a complex objective, the agent will break down the task into a series of smaller steps. For example, if tasked with building a website, the AI agent could develop the site structure, generate content for each page, design visuals, debug issues — all from a single command.

Scope of knowledge

AI chatbots operate within a narrow domain of knowledge, limited to what’s in their static knowledge base and available from integrations with tools like CRMs, calendars, and knowledge bases. To provide users with accurate and relevant outputs, they must be trained on a business’s unique data. The most advanced AI chatbots can retrieve data from specific external sources using APIs in a process called function calling, but only by user request. Nor can they synthesize this information on the fly, or autonomously add it to their knowledge base.

AI agents, in contrast, can retrieve and integrate data from their environment in real time to achieve their goals. Agents base their action on what they perceive in their environment, and can fill in the gaps as needed by connecting to other digital systems. For example, an AI agent can call on external data sources and tools — web searches, LLMs, databases, even other agents — then synthesize the new information to tackle a wider range of objectives.

Learning and adaptation

AI chatbots can improve but this typically requires human intervention. For example, users can indicate whether the bot responses were helpful or not, providing manual feedback to refine its outputs. Regular updates or retraining using techniques like retrieval augmented generation (RAG) are the primary way to improve accuracy and performance. The most advanced AI chatbots may use machine learning to improve response selection. However, the chatbot will still struggle to handle novel interactions that fall outside its training data.

By contrast, AI agents can continuously learn from interactions, improving and adapting by integrating interaction data into their knowledge base. Using techniques like reinforcement learning, AI agents can adjust their approach to better align with user preferences and business objectives. By analyzing and storing data from interactions in memory, then moving data in memory to its knowledge base, the AI agent creates a data feedback loop to autonomously self-improve over time.

Memory and context retention

AI chatbots can only retain context from a single session. Since they don’t hold memory, they can’t learn from unsatisfactory responses or mistakes without manual human input. This lack of memory also limits the ability to personalize interactions based on user preferences, past interactions, and relevant data. Once the session ends, the chatbot is back to square one.

AI agents, by comparison, maintain context of past interactions by storing and retrieving data from past interactions in their memory. This enables AI agents to adapt to user expectations and provide a more personalized experience with better responses. For example, an ecommerce AI agent could refer to past interactions in memory, purchase history in CRM, and the current conversation to deliver personalized recommendations and cart abandonment offers based on all the relevant context. Importantly, the AI agent will analyze its past actions and outcomes in memory, then add this data to its knowledge base to adapt its strategies over time.

How to choose: AI agent vs AI chatbot

Even though AI agents offer more advanced capabilities and can handle complex tasks, they aren’t the right choice for every business. As you evaluate your specific needs, resources, and goals to determine the best AI solution for you, here are the key factors to consider:

• Cost: AI agents have higher costs because they are part of complex agentic AI systems that require large datasets, complex integrations, and continuous monitoring to mitigate risk and ensure compliance. AI chatbots, by contrast, are more cost-effective to deploy and maintain. A custom AI chatbot can still provide great value without the significant resources required for advanced AI agents.

• Use case complexity: If your use case involves decision-making across domains, multi-step workflows, and multiple integrations, you likely want an AI agent. However, if you want to answer FAQs, handle repetitive interactions, or guide users through processes like onboarding, then a chatbot should work.

• Development and maintenance resources: AI agents require more technical capabilities and time for ongoing development. These sophisticated agentic AI systems typically require advanced skills in system integrations and machine learning as well as resources for oversights and monitoring. AI chatbots are less powerful but require far less specialized expertise to implement, and are easier to update.

• Data privacy and security: AI agents, with their ability to access other systems and data, typically require more robust security measures than AI chatbots. This is especially important if your use case involves sensitive user data or regulatory compliance. While chatbots are less versatile due to their limited scope, they are also easier to secure and audit.

• Scalability: AI agents are designed for a more dynamic range of use cases, so they may be better-suited for evolving user needs as your business grows. A business could even develop a team of agents, each with its specialized own use cases. By contrast, AI chatbots are suited to handling high volumes of user interactions, but they’re much less scalable to diverse needs.