Robotic process automation (RPA) is software that carries out rule-based, repetitive tasks across applications the way a person would: logging in, copying data between screens, filling forms, moving files. A bot follows a fixed script. It does not decide; it repeats. AI agents, by contrast, interpret, judge and choose their next step.
Why does RPA matter now that agents exist?
Many regulated firms already run hundreds of RPA bots, often built to bridge old systems that cannot talk to each other. Agents are arriving into processes where bots are already doing part of the work. The useful question is not which replaces which, but where each belongs.
RPA's strengths are predictability and auditability: the same input produces the same output, every time. Its weakness is brittleness. A changed screen layout, an unexpected field or a free-text request breaks the script and drops the case into an exception queue.
How does RPA work in practice?
A developer records or writes a script that follows a process step by step: open the claims system, read the policy number, open the policy system, copy the cover dates, paste them into the claim. The bot runs under a service account, often on a schedule. It handles the cases that fit the script and passes the rest to people.
Bots are governed like other software: change control, access reviews, and a named owner. Because they do the same thing every time, testing them is mostly a matter of checking the script handles the cases expected.
What does an example look like?
An insurer uses a bot to copy new policy data from its broker portal into its policy administration system each night. It works well until a broker sends a policy with an extra rider the bot's script does not know. The bot skips it and logs an exception. An agent could read the rider, work out what it is, and draft the entry for a person to approve. The pairing that works: the bot keeps doing the predictable copying, the agent handles the cases the bot cannot, and exceptions an agent cannot process still go to people.
How is RPA different from an AI agent?
| RPA bot | AI agent | |
|---|---|---|
| How it works | Follows a fixed script | Interprets the task and chooses steps |
| Input it handles | Structured, predictable | Unstructured as well: emails, documents, requests |
| When things change | Breaks | Adapts, sometimes wrongly |
| Testing | Check the script | Certify behaviour on real cases |
| Main risk | Stops working | Acts wrongly with confidence |
That last row is the key governance difference. A broken bot is loud. A mistaken agent is quiet, which is why agents need bounds enforced at the moment of action, held actions for people, and certification on real cases. An agent also tends to invite more trust from the people reviewing it than a bot does, which is a form of automation bias.
Where does RPA end and the agent begin?
Use RPA where the rules are fixed and the inputs are clean. Use agents where judgement or reading is needed. Before adding either, look at the process itself: automating a poor process only makes the poor process faster. How to redesign a process for AI agents covers that step, and choosing the first process to give an AI agent helps decide where to start. Good process owners matter more, not less, once agents arrive, which is the argument in agents raise the value of good process owners. See our guide to deploying AI agents for the wider picture.
Questions people ask
Will AI agents replace RPA?
In some processes, gradually. In others, RPA remains the cheaper and more predictable choice. Most firms will run both, with agents handling the cases bots drop.
Can an agent drive the same screens a bot does?
It can, through tools that operate user interfaces, but this is fragile and hard to bound. Where a system offers a proper interface for software, connect the agent to that instead.