5 key differences between an AI Agent & AI Chatbot

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Although these two terms might be used interchangeably, AI Agents and AI Chatbots are two fundamentally different types of software. AI Chatbots leverage LLMs (Large Language Model) like OpenAI’s GPT-4o to respond to FAQs leveraging knowledge-bases and can complete simple actions through tool calling (a.k.a.: function calling), like booking an appointment or providing the status of an order.

AI Agents are capable of doing this, and much more, where companies can provide high level goals like “help customers change their shipping address” which might seem simple at first, but actually requires working through multiple steps dynamically while ensuring compliance with company policies, e.g.:

Request the order number to check the order status

Determine if the order status is eligible for a change of shipping address (e.g.: orders that have already shipped can’t be changed)

Requesting the new shipping address

Determine if the new shipping address is valid, within the area of delivery and if it changes pricing in any way

Confirm the shipping address change and make the changes within the business system

Notify the user of the shipping address change

In the context of an e-commerce business, AI Agents can go much further than helping customers with FAQs, like responding to questions about payment methods, but actually assist with complex tasks like returns, modifying orders, etc., that cost businesses much more on a per resolution bases. For the first time, we have software that can complete human work. Tomasz Tunguz wrote a great piece about this, calling this new wave of software: “Service-as-a-Software”, where Agentic Systems are able to perform human labor, instead of serving as a tool to perform human labor.

Since the launch of ChatGPT in late 2022, it seems every software company has suddenly become an AI company. Just search for “AI Customer Support” in Google and you’ll get hundreds of vendors that claim to provide AI Chatbots or Agents, and seem very similar at first, but in this article we’ll help you cut through the noise and understand the key differences between simple “AI Wrappers” that don’t scale beyond simple uses cases, and the true innovators in the AI Application Layer building AI Agents that can help your business operate 24/7, at any scale.

Decision-Making & Reasoning

The first key difference between AI Chatbots & AI Agents is their ability to reason and make decisions. Although it’s possible to implement some basic form of reasoning through CoT prompting (Chain of Thought), this approach is limited and doesn’t scale well to handle a growing number of complex workflows. To understand how this looks like in the real world:

Legacy Chatbots use rule-based decision making. These are traditional phone trees (Press 1 for sales, Press 2 for support) or chatbots that require user input like 1, 2 or 3. Interesting insight: most businesses leveraging WhatsApp Chatbots in LATAM are still using these type of Chatbots!

AI Chatbots might use NLP (Natural Language Processing) to determine query intent and navigate pre-built workflows, while being able to answer questions from a knowledge-base usually hosted on vector databases like Pinecone or Qdrant.

AI Agents use Agentic Routing, which leverages LLMs like GPT-4o as a “Routing Agent” or “Orchestrator” to process incoming user queries and direct them to AI sub-agents based on their specialization, capabilities, or contextual relevance. This usually involves using NLU (natural language understanding), and intent detection to determine user intent.

Multi-Agent Systems

AI Chatbots are usually composed of a sole LLM, with a single prompt, connection to a knowledge-base to perform RAG (Retrieval Augmented Generation) and have access to tool use (function calling) to interact with external systems, e.g.: Calendar API to book appointments.

AI Agents usually rely on MAS (Multi-Agent Systems) where different LLMs work collaboratively, each playing specific roles, to achieve a common goal. In the previous point, I shared how using a Routing Agent can help direct user queries to the right AI Sub-Agent. Following the e-commerce scenario, each sub-agent can be responsible for a very specific task, e.g.: order tracking, changing a shipping address, returns, handle refunds, general inquiries, etc. Reducing the scope of an AI Agent is a great way to improve it’s reliability in task execution. Furthermore, these systems can leverage other LLMs to perform tasks across agents, e.g.: identify entities & variables & extract them from user queries (e.g.: identifying an order number and transforming it to the right format to make an API call).

Agentic Workflows

Legacy chatbots navigate pre-defined workflows, sequentially, with no awareness, context or flexibility to dynamically adjust it’s behavior based on an ongoing conversation. It’s just a traditional computer program executing rules, and expecting a pre-defined input like 1 or 2, yes or no, etc., in order to traverse the workflow logic. This is called deterministic system.

AI Agents often rely on “Agentic Workflows”, which are sequence of tasks that are managed and executed autonomously to achieve specific outcomes, such as “changing a shipping address”.

There are many ways these Agentic Workflows can be constructed and configured, e.g.: using computer code (e.g.: Langchain), or using a GUI (e.g.: Frontline).

Agentic workflows can be scripted, where a human defines the tasks and how they should be achieved at a high level (instructions, tools, policies, etc.) or unscripted, where the AI Agent can leverage instructions, available tools, policies, etc., to define the tasks that it needs to complete given a high-level goal, e.g.: help me with a refund. The AI Agent no only breaks down the query into executable tasks (e.g.: using query decomposition), but it handles the scheduling and execution of these tasks, while being able to react to task outcome and redefine the task plan based on a task execution outcome, e.g.: If the purchase is not eligible for a refund, a new task must be created to notify the user about the resolution. Some frameworks like LLM Compiler have introduced this AI Agent architecture to speed up execution, increase reliability and cost efficiency.

Adaptability

Legacy chatbots are inherently rigid systems that break down when encountering unexpected inputs or scenarios outside their pre-programmed paths. A typical interaction might look like:

User: "I need help with my order but I lost the confirmation email" Bot: "I don't understand. Please provide your order number" User: "That's what I'm trying to say - I don't have it" Bot: "I don't understand. Please provide your order number"

These systems can't adapt to handle alternative paths to goal completion, creating frustrating user experiences that often require human intervention.

AI Agents, in contrast, can dynamically adjust their approach based on available information and context. They typically leverage Directed Acyclic Graphs (DAGs) to model conversation flows, where each node represents a potential state or action, and edges represent transitions between states. Unlike linear decision trees, DAGs allow agents to:

Navigate multiple possible paths to achieve the same goal

Backtrack when a particular approach fails

Skip unnecessary steps when certain information is already available

Gracefully handle edge cases and unexpected scenarios

When faced with missing or unexpected information, they can:

Identify alternative methods to achieve the user's goal (e.g., looking up orders by email, phone, or shipping address)

Break down complex problems into smaller, manageable steps

Provide contextual explanations and guide users through alternative solutions

Maintain the broader context of the conversation while working through unexpected scenarios

For example, if a customer can't provide an order number, an AI Agent might respond:

"I understand you don't have your order number. No problem - I can help look up your order using other information. Could you provide the email address you used for the purchase? Alternatively, I can search using your shipping address or phone number."

This adaptability means AI Agents can handle a much wider range of real-world scenarios while maintaining a natural, helpful conversation flow - even when things don't go according to the "happy path."

Proactivity

Legacy chatbots and even basic AI Chatbots are purely reactive systems - they only respond when prompted by user input and within the narrow scope of what was explicitly asked. This creates gaps in customer service where potential issues aren't addressed until they become problems.

AI Agents, however, can proactively identify opportunities to assist customers based on context, historical data, and current conversation flow. For example, when helping a customer track an order, an AI Agent might proactively:

Notice the delivery date has been pushed back and offer to notify the customer of any further changes

Identify that the shipping address is in a region with known delivery delays and preemptively explain the situation

Here's how this might look in practice: User: "Can you check the status of order #12345?" Agent: "I see your order #12345 is scheduled for delivery on November 30th. I notice your delivery address is in an area currently experiencing weather-related delays. While your order is still on track, delivery times might be extended by 1-2 days. Would you like me to set up automatic SMS updates to keep you informed of any changes to the delivery schedule?"

This proactive approach helps prevent future support queries and enhances the customer experience by addressing potential issues before they arise. In the e-commerce context, this can significantly reduce support ticket volume and improve customer satisfaction by keeping customers informed and offering solutions before problems escalate.