Contents
- 1 What Is an AI Agent?
- 2 What Is a Chatbot?
- 3 Key Differences Between AI Agents and Chatbots
- 4 AI Agents vs. Chatbots: Their Roles in Automation
- 5 Chatbot vs. AI Agent: Use Cases and Examples
- 6 AI Agents vs. Chatbots: Which One is Right for Your Business?
- 7 The Future of AI Agents and Chatbots
- 8 Conclusion
It’s 2020. Your company is adopting a brand new chatbot—a solution that is supposed to make the lives of your support and sales departments easier. Some two years go by, and OpenAI drops its large language model, prompting the development of AI chatbots. Now, fast forward to 2025, when AI agents are promising to put chatbots to shame with their unparalleled capabilities.
What does all that mean for your business? Which side do you take in the AI agent vs. chatbot battle? Does your company need both? Or maybe you should ride the innovation wave and toss your chatbots into the metaphorical trash?
There are many questions to answer, and as a company offering AI agent development services, we’re happy to help. Keep reading to learn the difference between an AI agent and a chatbot, when each should be used, and what they can do for companies in Healthcare, Fintech, Insurance, MedTech, and Retail.
What Is an AI Agent?
AI agents are not your typical chatbot infused with artificial intelligence. They’re large language models that integrate into your business environment (CRMs, ERPs, databases, etc.), allowing them to analyze company-specific information and take action. That’s why they’re called “agents,” not “assistants:” An AI agent can act independently, make decisions, and carry out tasks with little human input.
AI agents are great for automating business processes, providing proactive and personalized customer service, and making dynamic decisions. Businesses from different industries can use AI agents for:
- Hospital administration and operations
- Medical billing and revenue cycle management
- Remote patient monitoring
- Chronic disease management
- Fraud detection
- Risk management
- Personal finance management
- Algorithmic trading and investment optimization
- Loan underwriting
- Credit scoring
- Claims processing
- Customer retention
- Risk assessment and underwriting
- Regulatory compliance
- Audit automation
- Drug discovery
- Smart medical device monitoring and maintenance
- Personalized treatment plans
- AI-assisted surgery
- Robotic process automation (RPA)
- Inventory and demand forecasting
- Dynamic pricing and promotions
- AI-driven customer personalization
- Autonomous checkout
- Theft prevention
What Is a Chatbot?
A chatbot is software designed to communicate with users, usually through text or voice. It follows predefined rules or uses AI to generate responses based on input. And while they can simulate conversation, most chatbots work within structured workflows.
For instance, a banking chatbot can check account balances or help reset passwords. A retail chatbot might recommend products or track an order. However, its capabilities are limited since chatbots just wait for input and respond based on scripted logic.
Even AI-powered chatbots, which use natural language processing (NLP) to understand intent, still need user input. They rely on existing data and can’t go beyond what they were programmed to do. And if a chatbot can’t handle a request, it either repeats information or redirects users to a human agent.
Key Differences Between AI Agents and Chatbots
If you’re still lost in the chatbot vs. AI chatbot vs. AI agent debate, here’s a quick recap:
- Chatbots are basic assistants that follow scripts.
- AI-powered chatbots are smarter conversational tools, but you still need to tell them what to do.
- AI agents are autonomous systems that make decisions and execute tasks without your input.
Chatbots are great for customer service and simple automation, but AI agents offer far more capability for businesses that need real-time decision-making. The difference isn’t just in complexity—it’s in what each system can actually do.
| Feature | Chatbots | AI-powered chatbots | AI agents |
|---|---|---|---|
| Interaction type | Scripted, keyword-based responses | Uses NLP to understand intent | Conversational |
| Decision-making | Follows predefined rules | Can adapt responses but doesn’t make decisions | Makes real-time decisions based on data |
| Context awareness | Limited; often forgets past interactions | Can retain short-term context | Remembers and learns from interactions over time |
| Reasoning | None | None | Close to human-like (remembers decisions, revises them based on new information, etc.) |
| Ability to learn | No learning; follows fixed workflows | Can improve responses with training | Learns from patterns and continuously improves |
| Dependency on user input | Requires user input to function | Can predict intent but is still reactive | Acts proactively; doesn’t always need user input |
| Workflow automation | None—just provides answers | Limited (e.g., filling forms, booking appointments, etc.) | Automates multi-step processes without human intervention |
| Data processing | Simple queries | Can analyze structured text input | Integrates with multiple data sources for real-time decision-making |
| Integration with business systems | Basic (i.e., customer support platforms) | Limited API access | Deep integration with CRMs, ERPs, IoT devices, financial systems, etc. |
AI Agents vs. Chatbots: Their Roles in Automation
While both chatbots and AI agents help automate internal and external processes, their capabilities are different.
| (AI) Chatbot | AI agent |
|---|---|
| Can schedule appointments and answer patient FAQs | Can monitor real-time patient data, detect early health risks, and alert doctors |
| Can help users check their account balance or explain loan options | Can assess credit risk, detect fraud, approve or deny transactions in real time, etc. |
| Can guide users through the claims process | Can verify policy coverage and process payouts automatically |
| Can provide troubleshooting tips for medical devices | Can predict equipment failures and schedule maintenance before a breakdown occurs |
| Can recommend products based on past purchases | Can track inventory, predict demand, and dynamically adjust pricing |
| Can answer HR-related questions about policies and benefits | Can manage workforce scheduling, optimize payroll, and detect burnout trends based on employee interactions |
| Can provide policy guidance | Can monitor transactions and operations in real time to flag risks and ensure regulatory compliance |
| Can help vendors track shipments | Can optimize supply chains by predicting demand fluctuations and automating order management |
Chatbot vs. AI Agent: Use Cases and Examples
As you can see, chatbots and AI agents are not the same: chatbots work for simple interactions and giving predefined answers, while AI agents cover complex decision-making and automation. This means that their use cases also differ.
A survey from LangChain discovered that most businesses use AI agents for research and summarization (58.2%). Personal productivity and task automation (53.5%) come next, with AI agents handling repetitive work to free up time for more important tasks. Customer service (45.8%) was the third most popular area, where AI agents assist with inquiries, troubleshoot issues, and speed up response times.
Chatbots, on the other hand, are still used mostly for answering frequently asked questions. According to an Intercom survey, the most significant use case for chatbots is sales (41%), where chatbots help customers find the right products, guide them through purchases, and even upsell. Customer support comes next (37%), handling common questions so human agents can focus on more complex issues. Finally, there’s marketing (17%), with chatbots sending personalized offers, collecting feedback, and keeping customers engaged.
Now, let’s look at some examples of industry-specific chatbots and AI agents to see their real-life applications:
| Industry | Chatbot | AI agent |
|---|---|---|
| Healthcare | HealthTap Provides automated medical advice based on a database of doctor-verified responses | Aidoc AI-powered radiology assistant that flags critical findings in scans |
| Fintech | Erica (Bank of America) Helps customers manage accounts and analyze spending | Upstart Uses AI to assess loan applicants beyond traditional credit scores |
| Insurance | Haven Life AI Bot Guides customers through life insurance applications | Roots Automation AI agent that handles policy processing and compliance tasks |
| MedTech | Bot MD AI-powered chatbot for doctors to quickly retrieve medical information | BioMind AI agent that assists doctors in diagnosing neurological disorders. |
| Retail | Nike StyleBot Helps customers find and customize shoes | Trax AI Monitors shelf stock and automates inventory replenishment |
AI Agents vs. Chatbots: Which One is Right for Your Business?
A chatbot is usually enough for helping customers with FAQs, guiding them, or handling straightforward requests, like:
- Booking appointments
- Providing account balances
- Giving basic financial advice
- Helping navigate policy details
- Product recommendations
- Order tracking
But if your business wants to take advantage of real-time data processing, decision-making, or workflow automation, developing an AI agent might be a better choice. For instance, AI agents can do the following unprompted:
- Detect and block fraudulent transactions in fintech
- Analyze and approve insurance claims without manual review
- Predict medical device failures and schedule maintenance
- Optimize inventory and adjust pricing dynamically
KPMG claims that AI agents are still in the early stages of adoption, but businesses are paying close attention. While only 12% of companies surveyed have fully implemented them, interest is growing—51% are actively exploring their potential, and 37% are running pilot programs. Over the next year, executives expect AI agents to take on administrative work (60%), support call center operations (54%), and assist in content creation (53%).
The Future of AI Agents and Chatbots
Chatbots and AI agents are evolving in different ways. Chatbots are becoming better at conversations, with improved natural language understanding and context awareness. However, they still assist rather than act and can’t handle complex automation.
AI agents, on the other hand, are shifting toward full autonomy. They already help with fraud detection, predictive healthcare, and supply chain optimization. However, some critical challenges need to be addressed before the widespread adoption of this technology:
- Reliability. Because AI agents rely on LLMs to control workflows, their unpredictability can lead to errors.
- Knowledge. Many teams lack the expertise to implement AI agents effectively. Employees need time to learn how to integrate and optimize them for real business needs.
- Time. Setting up AI agents isn’t instant. Ensuring they work well requires debugging, fine-tuning, and continuous monitoring.
Overall, we shouldn’t bet on the concept of chatbots vs. AI agents as adversaries because the businesses that combine the two will be the winners. Chatbots will manage customer interactions, while AI agents drive backend automation and decision-making.
Conclusion
Strictly speaking, chatbot vs. AI agent is not a competition. Chatbots are great for answering questions and guiding users through simple tasks like booking appointments or checking account balances. But AI agents go a step further: they analyze information, make decisions, and handle complex tasks without needing human input.
If your business needs more than basic automation, AI agents can help. At DevCom, we build AI solutions that can bring actual results: detect fraud, manage workflows, predict trends, and more. If you’re ready to see what AI can really do, we’re here to help.
