AI-powered chatbots vs. AI agents: What’s the difference?

What is an AI chatbot?

An AI chatbot is a digital tool that mimics human conversations, engaging users through text or voice interactions. Leveraging natural language processing (NLP) and machine learning, it interprets user messages and crafts suitable responses, making interactions feel more fluid and intuitive.

Chatbots are designed with structured guidelines or trained on specific data sets, enabling them to perform set tasks or respond to queries within a defined range.

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According to the Tidio study, 96% of respondents consider that companies utilize chatbots in order to ensure customer satisfaction. Chatbots excel at delivering fast and uniform responses to frequent queries, making them a dependable and economical option for managing routine customer service tasks, gathering essential details, and recommending useful resources.

Though this capability to comprehend context and manage queries outside predetermined dialogue patterns is insufficient. After an answer is crafted, it’s essential to supply between 10 and over 500 variations of how a customer might phrase the inquiry. This allows the chatbot to learn different ways the question can be asked and accurately deliver the appropriate response.

As a result, AI chatbot conversational agents become a powerful tool for repeated, straightforward tasks, but they grapple with free-flowing dialogues.

AI chatbot use examples

While ensuring 24/7 availability, AI chatbots and virtual assistants offer an affordable solution for managing large-scale, repetitive operations. Here are some examples of effective AI chatbot use cases:

Basic IT support

A company can use an AI assistant chatbot as the initial touchpoint for employees seeking assistance with IT concerns. It can help users to handle common troubles, such as printer connectivity issues or password resets, while sending more complex ones to the IT department.

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Book tickets to events

Depending on what they are buying, customers usually ask the same questions before making a purchasing decision.

AI chatbots can enhance customer experience by answering these questions. If you have a movie theater, you can customize your chatbot to show trailers for customers before they buy tickets. This not only helps clients make a choice but also increases sales.

Customer service FAQs

Retail companies should consider using a chatbot, as it can help answer frequently asked questions about item accessibility, delivery, and returns. The chatbot compares customers’ queries with scripted answers, delivering swift responses to frequent inquiries while alleviating the workload on human customer service representatives.

Restaurant reservations

To manage reservations effectively, a local restaurant group can implement an AI chatbot on its website. AI chatbot assistant communicates with clients by asking for general information such as date, time, and group number. Then it checks availability in a linked reservation system to finalize bookings or propose alternative time options.

Real-world examples of AI-powered chatbots

Here are a few real-world examples of chatbots from various industries that are designed to handle certain tasks and deliver answers to frequently asked questions:

Sephora’s virtual assistant

This chatbot AI assistant provides customers with personal beauty recommendations, product details, and tutorials. It ensures clients make a perfect purchasing decision as fast as possible. It’s an ideal example of how companies can mix AI chatbots with E-commerce to achieve maximum efficiency.

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Duolingo MAX

This app has two AI-powered features for learners: Roleplay and Explain My Answer. In Roleplay, users can practice their language skills with AI characters with feedback on the quality of answers, while in Explain My Answer, GPT-4 is used to provide explanations to users’ questions.

H&M’s mobile app

A smart search powered by an AI chatbot assistant helps clients to give information about their orders and assists customers in locating information from publicly accessible customer service resources. The chatbot is also used to answer common queries such as missing items and refunds.

What is an AI agent?

It uses NLP (natural language processing) and LLM development (large language models) to effectively comprehend, and address queries.

AI agents are able to evolve through interactions, comprehend context, and adjust their behavior to accomplish specific goals. In contrast to chatbots, AI agents can handle uncertainty, make independent decisions, and carry out multi-step strategies to address complex problems. As a consequence, they become a perfect choice for dynamic and demanding tasks.

According to Campus Technology, AI agents are viewed as the driving force for workflow efficiency and budget optimization. By 2025, 85% of companies will utilize them to optimize operations, boost efficiency, and elevate customer engagement. To learn more about Agentic AI, please watch a video:

AI agent use examples

If you want to use artificial intelligence to solve tasks that need contextual comprehension, decision-making, and the capacity to adapt based on experiences, applying an AI agent will be your best choice.

Here are some examples of how you can use it in your work:

Automated content curation

To personalize content for subscribers, a digital media company may develop an AI agent. By analyzing engagement patterns, browsing history, and trending topics, it constantly refreshes users’ feeds with appropriate videos, articles, and podcasts, which leads to customer loyalty and the duration of user activity on the platform.

Intelligent supply chain management

A large-scale electronics retailer could leverage an AI agent to enhance supply chain efficiency. By analyzing supply chain quality, inventory levels, sales data, and outside influences like financial metrics and weather, the agent can optimize order volumes, forecast demand, and redirect shipments in real time.

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Career development assistant

To assist young professionals and students with employment seeking, a professional social network can build an AI agent.

As a result, it will scrutinize business patterns, user skills, and professional aspirations to review cover letters and resumes, propose customized job prospects, suggest suitable skill advancement courses, and deliver individualized interview coaching.

Real-world examples of AI agents

Here are some examples of AI agents whose cutting-edge features help businesses to be on the top of their game:

HostAI is an AI agent made for managing hospitality operations and short-term lodging administration. It computerizes tasks like scheduling, maintenance requests, guest interactions, and revenue optimization.

HostAI asserts that it manages more than 80% of guest interactions, provides instant responses to inquiries, and even oversees voice calls through AI-driven automation.

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MultiOn is famous for developing AI agents that can accomplish sophisticated online operations from beginning to end in place of users. These chatbots can understand user requirements and efficiently carry out diverse online processes across multiple platforms and services using minimal inputs.

For example, a virtual agent chatbot can help you make a restaurant reservation by searching booking websites, checking details, and verifying availability.

Sender is an AI agent built for decentralized finance (DeFi) operations on blockchain networks. It seeks to convert users’ intentions into blockchain-based operations, streamlining intricate DeFi processes across multiple platforms and protocols.

To provide a comprehensive ecosystem for crypto users, the agent merges with various DeFi platforms, such as lending services, NFT marketplaces, and decentralized exchanges.

What is the difference between AI agent and chatbot?

Both AI chatbots and agents are primarily used to help businesses and individuals, especially in terms of AI customer service. In their essence, they are developed to comprehend human communication and then to respond or take action. Their biggest advantage is that they are always ready to help you solve problems or answer questions.

It’s easy to mix up AI agents and AI chatbots because of their similar capabilities to communicate with users through natural language. If there is a sophisticated chatbot and a conversational agent, the distinction between them can be unclear, which is why sometimes these terms can be interchangeable.

Here are some primary differences you should know before choosing either of them:

Interaction complexity

AI chatbots are designed to manage simple, text-based interactions within a defined framework. They excel at assisting users with basic tasks, answering frequently asked questions, and delivering information from structured data sources.

Most chatbots count on fundamental natural language processing and pattern recognition to analyze user queries and choose suitable answers from a prearranged set of replies.

Sophisticated AI agents utilize decision-making mechanisms, context awareness, and advanced natural language understanding to deal with unclear demands and adjust their strategy according to real-time feedback and evolving circumstances.

Learning and adaptation

Traditional chatbots generally count on predefined response patterns, which limit their ability to handle real human conversation and dynamically adjust to different situations.

More sophisticated implementations can integrate machine learning models to eventually improve response selection, though this learning is generally confined to their designated domain. Even if chatbots with GPT integration services are constantly updated, it is still hard for them to deal with queries or situations outside pre-programmed solutions.

AI agents employ flexible models and continuous learning algorithms that adapt with every interaction.

By adjusting their approach in response to user input, these systems can draw insights from preceding experiences to work on new situations. The use of techniques such as transfer learning and reinforcement learning also allows agents to extend their abilities across various topics, making them more effective and adaptable in practice.

Task completion capabilities

Chatbot expert systems are mainly made for particular, restricted tasks. Their work is outstanding on the subject of managing basic transactions, assisting users through structured workflows, and giving answers to frequently asked questions. But their abilities come up blank when it comes to facing complicated, multi-step tasks that stand outside their limited programming.

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AI agents step up their game by dealing with complex, multi-stage operations across numerous services and platforms.

For example, if you want to plan a trip, from a single command, an agent can assist you in researching destinations, booking hotels, comparing prices, and even proposing activity ideas. Unlike a chatbot AI assistant that simply follows the script, an AI agent solves problems in real time, dynamically accommodating recent information.

Scope of knowledge

The knowledge domain of most chatbots is usually limited to a definite industry, service, or product, as their information base is frequently structured to the data gathered during training or through occasional updates.

For example, a chatbot for a car dealership company may help you get the information about the availability and price of only those vehicle models that they sell.

Although some sophisticated chatbots can retrieve data from external databases or APIs, they typically struggle to integrate information from multiple sources or independently broaden their knowledge. At this point, even the use of ChatGPT can be more effective if you want to learn as much information as possible.

AI agents generally possess a broader knowledge base. These systems can access real-time data streams, leverage vast language models, and multiple external sources to collect and process information in real time. They are capable of reasoning across different domains, making logical deductions, and even generating new insights by integrating existing information in a new way.

As a result, this extensive knowledge base allows them to manage numerous queries with impressive complexity and adaptability.

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How to choose between AI agent and AI chatbot?

It may seem there is no point in deciding between AI agent and AI chatbot when the first one offers more advanced capabilities.

However, not every business situation really needs them. To choose between them, you have to carefully evaluate your goals, needs, and resources. Here are some key factors you have to take into consideration to make the right choice:

Complexity of use case

Estimate the intricacy of the task you want to automate. A chatbot will be great for repeated, straightforward interactions, such as providing clear guidance on simple operations or answering frequently asked questions.

Though it’s better to take a look at AI agents if your application scenario requires making informed choices across diverse fields, handling multi-step workflows, or coordinating interactions between various systems.

Data privacy and security concerns

The choice between an AI agent or AI chatbot can significantly influence your data processing strategies if your application needs to adhere to strict regulatory compliance or handle confidential data.

Due to their more limited scope, chatbots may be simpler to audit and secure. AI agents are potentially more vigorous because of their more expansive access to data and systems, and consequently, they need more powerful protection strategies.

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Budget conditions

AI chatbots are more appropriate for companies with limited resources because they are budget-friendly to apply and maintain. If you work with a tight budget that doesn’t allow you to build a sophisticated and usually expensive AI agent, then applying a well-designed AI assistant chatbot will be a good idea that still brings value to your business.

Scalability requirements

Evaluate your future development plans and possible increases in user engagement. Chatbots are good at handling significant amounts of simple queries but may have difficulties with scalability for complex tasks. Built for highly adaptive settings, AI agents usually provide advanced scalability for a broad range of changing user needs.

Development and maintenance resources

Evaluate your team’s technological expertise and allocate time for ongoing enhancement. Chatbots are usually easier to maintain and need less specialized expertise.

As AI agents are more robust, that is why, besides constant upgrades and monitoring, they require specialists with extensive knowledge of systems integration, natural language processing, and machine learning.

AI agents and chatbots: Conclusion

Driven by the capacity to automate and streamline complex processes, AI agents have already proved themselves in various industries. From healthcare to finance, these systems showed their best in processing transactions, managing customer feedback analytics, and working with queries that previously needed considerable human effort.

It may seem that the development of traditional chatbots is lagging behind in comparison with AI agents, and their time is coming to its end, but this is not the case. We see how effectively they advance integration with other business systems and user experience, which still makes them one of the best examples of generative AI consulting.

We live in an era when implementing AI technologies into your business is no longer an innovation but a necessity to sustain a competitive edge on the market.

To be successful in using AI technologies today means not knowing the best one but learning how both of them work and selecting the most appropriate one for your current situation to benefit your business today and in the future. No matter what system you choose — an agent, a chatbot, or a combination of them — it’s undeniable that the decision to use them will change your business for the better.