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Agentic AI
Chatbot vs. Agentic AI: which is right for your business? Understand the key differences, use cases, and decision points to choose the best automation solution.
Most organizations already use chatbots for customer support, sales, or IT helpdesk automation. Recent data indicates that 87.2% of consumers have had neutral or positive experiences with chatbots, and 62% prefer using digital assistants over waiting for human agents. But as artificial intelligence advances, a new question arises:
When should you stick with traditional chatbots, and when is it time to upgrade to an agentic AI chatbot?
This distinction is crucial. Traditional chatbots can handle repetitive tasks but lack intelligence beyond scripted responses. On the other hand, AI agents go beyond answering customer queries. They can retrieve real-time data from internal sources like help centers and CRMs for accurate, consistent responses, and also learn from interactions to refine their decision-making.
The challenge is understanding chatbots and AI agents and knowing which solution aligns best with your business processes.
In this guide, we’ll break down the differences, best use cases, and key decision points to help you determine the right fit for your business operations.
Traditional chatbots have been common in customer service, but more advanced agentic chatbots are now taking their place. The difference lies in how they handle customer queries, business processes, and complex tasks. Understanding these distinctions can help decision-makers determine whether they need a simple chatbot or an agentic chatbot.
Example:
Traditional chatbot:
"Please upload your documents. Your claim is being processed."
Insurance Agentic AI:
"Hi Alex, I’ve reviewed your accident report and uploaded it to your case file. Now, I’m submitting it to Claims Review. This usually takes 1–2 business days. I'll update you once it’s approved, and then guide you to the next step for payment setup."
Conclusion:
Agentic AI transforms the insurance claims experience from passive waiting into active, transparent guidance. By clearly communicating each action and next step, it builds client trust, reduces uncertainty, and creates a smoother, more reassuring journey compared to traditional chatbots.
A traditional chatbot is a rule-based system that follows predefined scripts and decision trees to respond to user queries. These chatbots function within strict parameters and rely on human agents or developers to program their responses. If a user’s question falls outside the script, the chatbot either:
One of the earliest examples of a rule-based chatbot is ELIZA, developed in the 1960s at MIT. ELIZA mimicked human-like conversations by following predefined patterns, but it lacked the ability to:
While modern traditional chatbots have improved, they still operate within the same logic: providing structured responses but lacking true adaptability.
An agentic AI chatbot is more than just an automated responder—it retrieves information in real time, takes action, and adapts dynamically to user interactions. Unlike traditional chatbots, which follow scripted flows, and non-agentic AI chatbots, which generate responses without autonomy, agentic AI chatbots actively engage with systems, make independent decisions, and execute tasks within conversations rather than relying on human oversight.
Companies looking to handle complex tasks with minimal human intervention may benefit from adopting agentic chatbots.
A real-world example of an agentic chatbot in action is Intercom’s Fin, which redefines how AI-driven customer service works.
Many chatbots today provide automated replies, but they often lack true autonomy and require human intervention to complete tasks. Intercom’s Fin is different. It is a third-generation AI chatbot designed to understand complex queries, take independent actions, and learn from interactions.
Unlike traditional AI-powered assistants that rely on pre-scripted responses or LLM-based (large language model) text generation, Fin is built with the Fin AI Engine™, an advanced architecture that integrates knowledge, executes actions, and autonomously manages interactions.
Here’s what sets Fin apart from typical AI chatbots:
The biggest challenge with AI chatbots today is their inability to act beyond providing information. Many businesses adopt chatbots that generate LLM-based responses, but these bots struggle with real-world customer service applications due to their lack of structured knowledge integration and action-taking abilities.
Compared to Zendesk’s AI chatbot, which relies heavily on static knowledge bases, Fin dynamically pulls from multiple sources and executes real-time actions, making it far more effective in complex support scenarios.
Companies that implement Fin see tangible benefits, including:
Since its launch, Fin has been adopted by over 4,000 businesses, helping them reduce customer wait times, automate resolutions, and improve support efficiency.
A notable company using Fin is Anthropic, an AI research company. With AI development requiring highly technical customer support, Anthropic uses Fin to provide instant, AI-driven responses to customers, allowing human agents to focus on higher-level inquiries.
Fin goes beyond basic chatbots by understanding, acting, and improving over time. For companies wanting full automation without losing personalization, Fin sets the standard.
While Fin is a leading example of an agentic chatbot, businesses must determine what level of automation suits their operations best. Here’s a framework to help you decide.
Choosing the right chatbot for your business is about aligning technology with your operational needs, customer expectations, and long-term goals. The level of automation, complexity of queries, and ability to execute tasks autonomously should all factor into your decision. Here’s a guide to help you determine which chatbot type is the best fit.
If your business simply needs a chatbot to handle FAQs or direct customers to the right department, a traditional chatbot is sufficient. If you require conversational AI that understands context but still relies on human intervention for task execution, a non-agentic AI-powered chatbot is the better choice. However, if you need an AI agent that not only responds but also completes tasks autonomously, an agentic chatbot is the way forward.
If your chatbot only needs to respond to routine queries using predefined answers, a traditional chatbot will work. If your business requires a chatbot that can provide AI-generated responses based on historical interactions, a non-agentic AI chatbot is more suitable. But if you need an AI system that understands the problem, takes action, and improves its responses over time, then an agentic chatbot is necessary.
A traditional chatbot is ideal for providing information but cannot execute real tasks. If your AI needs to offer recommendations and guide customers to solutions but still requires human approval for execution, then a non-agentic chatbot is the right choice. However, if your chatbot must take action autonomously, such as handling refunds, scheduling meetings, or updating CRM records, then an agentic chatbot is the best solution.
If you only need a chatbot that follows scripts and doesn’t need to adapt, a traditional chatbot is enough. For AI-driven responses that require occasional updates and retraining, a non-agentic chatbot will work. But if your business needs a chatbot that actively learns, refines responses, and adapts to new scenarios without frequent human intervention, then an agentic chatbot is the best choice.
If your chatbot has a single purpose and doesn’t need to evolve, then a traditional chatbot works fine. If your business needs an AI chatbot that can scale to new use cases but requires manual tuning, a non-agentic chatbot is better. But if you require a chatbot that can scale autonomously, learn from new interactions, and evolve without frequent manual updates, then an agentic chatbot is the right investment.
To learn more about building custom AI agents for your business, check out this guide:
How to Build a Custom AI Agent for Your Business
A chatbot follows predefined scripts and responds to user queries without taking independent action. On the other hand, an agentic chatbot makes decisions, executes tasks, and improves over time without human intervention.
Traditional chatbots are cheaper and easier to implement but have limited capabilities. AI agents, while more expensive, reduce operational costs by automating workflows, improving customer satisfaction, and handling complex tasks that would otherwise require human agents.
An agentic chatbot is best when your business requires automation beyond just answering questions. If you need an AI system that processes refunds, schedules meetings, updates CRM records, or makes autonomous decisions, an agentic chatbot is the right choice.
Traditional and non-agentic chatbots assist with AI-generated responses but still need human oversight to complete tasks.
Agentic chatbots, like Intercom’s Fin, go beyond answering questions—they take action, execute workflows, and continuously learn without manual intervention.
If you need basic automation, a traditional chatbot works. If you want AI-powered responses but still require human approval for actions, go for a non-agentic chatbot. But if your business requires full automation, decision-making, and multi-step execution, an agentic chatbot is the right investment.
Want to supercharge your business with AI? See how agentic chatbots can revolutionize your operations. Book a demo with Arcee AI today!
So you want to train a custom language model, and you do have the requisite large set of text data. But how do you know that the data is *really actually ready* for model training? Our researchers here at Arcee AI tell you what to look out for.
2024 is poised to be the year of AI adoption across businesses and the consumer sector. At Arcee, our goal is to continue sharing insights from both our team and other domain experts, so that we empower every business with the knowledge and ability to leverage this phenomenal technology.
With Arcee Orchestra, you’ll transform complexity into simplicity, see faster collaboration across departments, and ultimately drive business growth—one automated workflow at a time.
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