RPA vs AI agents: which one should automate your process?

The definitions on this page come from the vendors and analysts who coined or sell these tools, read on October 1, 2026: Gartner for hyperautomation, UiPath and Microsoft for RPA, Anthropic for agents. Vendors have an interest in their own category, so we quote them for what the tools do and keep the recommendations to ourselves. Those recommendations are the grid our AI automation agency applies when it maps a process with you.

RPA vs AI agents, side by side

RPA repeats what it was shown; an AI agent decides what to do next. Every other difference in this table follows from that one.

Read the table one step of your process at a time: each line tells you which tool fits that step.

What RPA does well, and where it breaks

RPA shines on repetitive, rule-based work in applications nobody can change. UiPath, one of the main RPA vendors, defines it as software robots that “automate repetitive, rule-based tasks like data entry and system integration” and that mimic “human actions in interacting with screens and systems”.

Microsoft describes the same reach in its own RPA product, Power Automate desktop flows: they automate “both legacy applications, such as terminal emulators, modern web and desktop applications, Excel files, and folders”, and interact with the machine “by using application UI elements, images, or coordinates”. That is the strength of RPA: it works where there is no API, on the screens your team already uses.

It is also its weak point. A robot that relies on a screen depends on that screen staying the same. When the application is updated, a field moves or a pop-up appears, the robot stops until someone fixes it. And a robot does what it was told: if an invoice arrives in a new layout or an email asks for something unusual, it has no way to interpret it.

What an AI agent adds

An AI agent brings judgment where the input varies. Anthropic, which builds the Claude models, draws the line between two kinds of systems: workflows, where models and tools are “orchestrated through predefined code paths”, and agents, “where LLMs dynamically direct their own processes and tool usage”.

That flexibility has a price, and Anthropic says so: “The autonomous nature of agents means higher costs, and the potential for compounding errors.” Its advice is to start with “the simplest solution possible”, which “might mean not building agentic systems at all”.

Agents usually reach your systems through APIs and tools, which is sturdier than a screen. Some model providers also let an agent drive a screen directly, through computer use. Anthropic’s documentation states that computer use “has unique risks distinct from standard API features”, recommends “a dedicated virtual machine or container with minimal privileges”, and warns that its latency “might be too slow compared to regular human-directed computer actions”. For repetitive screen work, a robot remains the steadier tool today.

Our page on AI agents for business shows the kinds of tasks we hand to agents, and the checks we put around them.

Hyperautomation, without the buzzword

Hyperautomation is a word for combining tools, and for treating automation as a cycle. Gartner put it at the top of its strategic technology trends for 2020, in a press release of October 21, 2019, and defined it as “the combination of multiple machine learning (ML), packaged software and automation tools to deliver work”.

Gartner adds two points that are still useful. First, hyperautomation covers “all the steps of automation itself (discover, analyze, design, automate, measure, monitor and reassess)”, so measuring and monitoring are part of the job. Second: “RPA alone is not hyperautomation.” Robots are one tool among several, next to workflow engines, document AI, rules and, today, AI agents.

Stripped of the label, the method is plain. Map the process, choose the right tool for each step, measure what it saves, and keep watching it after launch. That is how we run every automation project, from the first map to the monitoring.

Rule, robot, agent or person: choose per step

Choose the tool step by step, by asking what each step needs.

Two habits keep this grid honest. Give the agent only the steps that need judgment, so the rest stays cheap and predictable. And put a person on every step whose mistake would be expensive. n8n, for example, can pause an agent before a chosen tool runs and send the request to a reviewer, who approves or denies it.

If you are weighing workflow engines for the rule steps, our comparison n8n vs Zapier vs Make sets their prices and data rules side by side.

One process, three tools: a supplier invoice

Most real processes mix the three. Here is how a supplier invoice can flow when the accounting system is an older application with no API. This example illustrates the method and describes no client.

1. The invoice arrives by email. A workflow picks up the attachment and files it.
2. An AI step reads it. It extracts the supplier, the amounts, the dates and the order number, and returns them as structured fields. You can try this step on a sample invoice, or on the pasted text of one of yours, in our document extraction demo.
3. Rules check it. The workflow matches the order number and the amount against the purchase order. A match goes on; a mismatch goes to a person.
4. A robot keys it in. Because the accounting application has no API, an RPA robot enters the validated fields on its screen.
5. A person approves the exceptions. Anything the rules could not match, or above a set amount, waits for a human yes.

Each tool does the part it is good at. If the accounting system gains an API later, step 4 moves from a robot to a workflow call, and nothing else changes. Our accounts payable automation page shows how we automate supplier invoices from inbox to posting.

Before you replace your robots

If you already run RPA, start by sorting the robots you have. A short review usually does it.

• Keep the robots that run reliably on stable screens. Rebuilding them gains nothing.
• Move to an API workflow the robots that work on applications that now offer an API. They break less and cost less to run.
• Add an AI step where a robot fails because the input varies: a new invoice layout, a free-text field.
• Hand to an agent only the steps where someone today reads the case and decides what to do next.
• Write down an owner for every automation, robot or agent, with an alert when it fails.

When the process spans several systems and teams, our business process automation work starts with that inventory, on one real case followed from start to finish.

Find the right tool for each step of your process

Picture the repetitive steps running on their own, and your team spending its time on the cases that need a person. In a free 30-minute assessment, bring one process you want to automate and the applications it touches. We tell you which steps fit a rule, a robot, an AI step or an agent, and what to build first. The scope and price are fixed in writing before we start, and every workflow we deliver ships with an error alert, documentation and accounts in your name.

Book your free 30-minute assessment. We reply within one business day.