On this page
Key takeaways
- RPA repeats recorded steps; AI agents reason about what to do.
- RPA is reliable on stable screens and structured data; agents handle variation.
- Many good systems use both: an agent decides, RPA or APIs execute.
The core difference
Robotic process automation (RPA) records and replays steps in a user interface: click here, copy this field, paste it there. An AI agent reads the situation, decides what needs to happen and uses tools to do it. RPA follows a path; an agent chooses one.
Side by side
| RPA | AI agents | |
|---|---|---|
| Input | Structured, predictable | Messy, varied, natural language |
| Change tolerance | Breaks when screens change | Adapts to variation |
| Decision-making | None beyond rules | Plans and chooses steps |
| Testing | Deterministic | Needs evaluation sets and monitoring |
| Best for | Legacy systems without APIs | Requests that need reading and judgement |
When to use each
- Use RPA or API automation when the steps never change and the data is clean.
- Use an agent when every request is slightly different and someone currently has to read and decide.
- Use both when an agent needs to act in a legacy system with no API.
How they combine
A common pattern: an agent reads an incoming request and decides what should happen, then calls a deterministic automation (an API call, or an RPA bot for older systems) to execute it. The agent handles judgement; the automation handles repetition. Approvals sit between the two for anything risky.
Moving from RPA to agents
- List your bots and note which ones break most often.
- Identify where a person still reads or decides before the bot runs.
- Put an agent at that decision point, and keep the stable bot steps as they are.
- Add approvals and logs, then retire bots only when their steps are covered by APIs.
Frequently asked questions
Not entirely. RPA remains useful for stable steps in systems without APIs. Agents replace the human judgement around it.