AI agents, chatbots and RPA: what is the difference?

A chatbot answers questions in a conversation, RPA (robotic process automation) repeats fixed clicks and keystrokes in software exactly as they were recorded, and an AI agent works through a task: it reads untidy input, decides the next step from your rules and the context, and uses tools such as your ERP or email to act. A person approves the steps that carry risk. Most businesses end up using more than one of the three, each for the work it suits.

What each one does

Chatbots

A chatbot holds a conversation. Older chatbots follow a decision tree: the customer picks "Track my order", types an order number, and the bot looks it up. Newer ones use a language model to understand free text and answer from a set of documents.

Either way, the chatbot's job ends with a reply. It can tell a customer the return window is seven days. It does not usually raise the return, book the pickup and update the order in your system.

RPA bots

RPA records a sequence of actions in software and replays it. Log in to the GST portal, download the GSTR-2B file, save it in a folder, open Tally, import it. RPA is fast and predictable when the screens and the data never change.

It struggles with anything it was not recorded for. A new field on the portal, a supplier who sends a PDF where everyone else sends Excel, or a scanned invoice with a handwritten note will stop it or, worse, make it put data in the wrong place.

AI agents

An agent is given a goal, a set of tools and rules. It reads the input, which can be an email, a scanned document or a WhatsApp message in Hinglish, works out what it is, decides which tool to use, checks the result and either finishes or hands over to a person.

An agent copes with variety because it reads the content the way a person would. That same flexibility means it can make mistakes a fixed script would never make, so it needs checks, logs and approvals built in.

One process, three ways

A textile trader in Surat receives around eighty purchase orders a week from retailers. They arrive as emails with PDF attachments, Excel sheets and sometimes a photo of a handwritten order. Someone in the office types each one into Tally as a sales order.

• A chatbot could answer retailers who ask "Has my order been received?" if it can see the order list. It would not enter the orders.
• An RPA bot could enter orders that arrive in one fixed Excel layout. Every other format would still need a person, and a layout change would break it.
• An agent could read each order in whatever format it arrives, match the retailer and the fabric codes to the masters in Tally, prepare the sales order, and send anything unclear, such as an unknown design number or a rate that differs from the price list, to the office for a decision.

In practice the trader might use the agent for reading and preparing orders and keep a simple scheduled import, which is close to RPA, for posting the approved ones.

How to choose

A chatbot suits questions with known answers

Store timings, fee schedules, leave rules, delivery status. If the work is answering, and the answers live in documents or one system, a chatbot or a copilot is enough. Keep it simple.

RPA suits stable, high volume steps

If the input is always structured, the screens rarely change and every case follows the same path, RPA is cheaper to run and easier to audit. Many finance teams use it for downloads, uploads and report generation.

An agent suits variety and judgement within rules

If the input varies, the work needs reading and deciding, and the decision can be written down as rules with exceptions, an agent fits. Invoice matching, ticket triage, KYC document checks and drafting replies are typical.

Cost and control matter too. A chatbot is the cheapest to run and the easiest to explain to an auditor. RPA runs at almost no cost per item once built. An agent pays for model usage on every item and needs the most testing, so it should be used where its reading and judgement save real work.

A quick test: ask the person who does the work how many different formats and exceptions they see in a normal week. If the answer is "one or two", consider RPA. If they laugh, consider an agent.

Where they work together

The three are often parts of one flow. A support agent reads a complaint on WhatsApp, works out that the customer wants a replacement and prepares the request. A person approves it. A fixed integration, or an RPA step for an older system without an API, creates the replacement order. A chatbot answers the customer's later question about when it will arrive.

In AksharAI these pieces sit in one workflow: agents for the reading and deciding, fixed steps for the parts that should never vary, and approval points between them. See logic and workflows and the agent studio for how they connect, and the glossary for the terms.

Where a person approves

The more an automation can do, the more carefully you place approvals.

• Chatbots mainly need a person to own the content they answer from and to review conversations the bot could not handle.
• RPA needs a person to check exception reports and to approve any change to the recorded steps.
• Agents need a person at every step that moves money, changes a customer record, sends something binding or touches personal data in a new way. Where a person must approve sets out how to place those checkpoints.

Limits to plan for

• Agents make plausible mistakes. A wrong answer from an agent can read as confidently as a right one. Test on real past cases before launch and keep reviewing logs afterwards.
• RPA breaks quietly when screens change. Someone has to watch for failures and update the recording after every portal or software update.
• Chatbots go stale. If nobody updates the documents behind them, they keep giving last year's answer.
• Running costs differ. RPA has licence and maintenance costs; agents add model usage that grows with volume. The cost to build estimate gives a rough range for a first agent.
• None of them fixes a broken process. If the rules are unclear to your own staff, write them down first.

Next step

Pick one process and describe a normal week of it: how many items, in which formats, with which exceptions. The use case finder helps match that description to a starting point, and the agent readiness check shows what to prepare before building.