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RPA replays clicks on a screen; an AI agent reads, decides and calls tools.
RPA for stable screens with no API; AI agents for steps that need reading or judgement; plain workflows over APIs for the rest.
Use RPA (robotic process automation) when a step happens inside an application that offers no API, export or email feed, the screens rarely change, and the rules are fixed. The bot types and clicks exactly as a person would.
Use an AI agent when a step needs reading or judgement: understanding an email, pulling fields out of a document that never looks the same twice, or deciding which of several actions comes next. The agent works through tools you give it, and a person approves anything that moves money or reaches a customer.
For everything in between, plain workflow logic over APIs is usually cheaper and sturdier than either. Our business process automation work starts by deciding, step by step, which of the three fits.
| Compared on | RPA | AI agent |
|---|---|---|
| How it works | Replays recorded or scripted clicks and keystrokes on an application's screen | A language model reads the input, plans, and calls tools (APIs, databases, other systems) until the goal is met |
| Handles unstructured input | No; needs fixed fields in fixed places | Yes; emails, PDFs, chats and scanned forms |
| Same input, same result | Yes | Usually, not always; outputs vary and must be checked |
| What breaks it | A moved button, a new screen, a slow page, a changed login | Unclear instructions, missing tools or data, inputs unlike anything tested |
| Upkeep | Bots need fixing whenever the screens change | Prompts, tools and a test set need maintaining as the process changes |
| Audit trail | Logs of each click and screen | Logs of each tool call, the model's reasoning summary and who approved what |
| Best fit | Old desktop or web systems with no API, high volume, fixed rules | Reading, classifying, drafting and deciding the next step |
Stable legacy screens with no API, export or email feed.
A government portal, an old ERP screen or a bank's corporate site that has no API and no export. If the screens are stable and the steps never vary, a bot is the only practical automation, and it can run every night without a person.
Documents, emails and chats that need reading before anything can happen.
Supplier invoices in twenty layouts, customer emails asking three things at once, WhatsApp enquiries in English and Tamil. An agent, or a simpler model step, turns these into structured data and a proposed action. RPA cannot read them at all.
Direct API workflows beat screen bots whenever an API exists.
If both systems have APIs, a workflow in a tool such as n8n moves the data directly. It is faster than driving a screen, does not break when the interface is redesigned, and costs less per run than a model call.
AI reads, workflows move data, people decide, bots handle API-less screens.
A common pattern: an AI step reads the incoming invoice, a workflow checks it against the purchase order through the accounting system's API, a person approves the exception, and an RPA bot enters the approved payment in a bank portal that has no API. Each tool does only the part it is good at.
AI takes over the reading and deciding; APIs replace screen bots where possible; RPA stays for screens with no other way in.
Partly. Many steps that were automated with screen bots because the input was messy are better done today by a model that reads the input and a workflow that writes the result through an API. Agents also cope with small changes that would stop a bot.
But agents do not remove the need to reach systems that only have a screen, and they add costs RPA does not have: model calls on every run, outputs that vary, and the need for a test set and human approval on anything consequential. The practical answer is to move the reading and deciding steps to AI, move data through APIs wherever they exist, and keep screen bots only for what is left.
Process automation, AI agents and workflow automation services, plus the related comparisons.
For whole processes, see business process automation. For agents that read, decide and act, see AI agent development. For single workflows such as approvals and data entry, see AI workflow automation. The difference between a fixed workflow and an agent is covered in AI agent vs workflow automation, and RPA itself in what is RPA.
RPA repeats fixed clicks and keystrokes on an application's screen. An AI agent uses a language model to read inputs, decide what to do and call tools to do it. RPA needs structured input and stable screens; an agent handles unstructured input and changing situations but needs testing and human approval for consequential actions.
Yes, and in larger processes they often should. The agent or a model step reads and decides, a workflow moves data through APIs, a person approves exceptions, and an RPA bot handles only the systems that have no API.
Per run, a bot usually costs less because it makes no model calls. Over time, upkeep often decides it: bots need fixing whenever screens change, while agents need their prompts, tools and test set maintained. Where an API exists, a plain workflow is usually cheaper than both.
We use screen automation only when a system offers no API, export or email feed, and we say so up front. Most of our automation work runs on APIs and workflow tools such as n8n, with AI steps for reading and judgement and a person approving anything final.
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