RPA — robotic process automation — has been around for years. It automates by mimicking a person: click here, copy this field, paste it there. AI agents work differently: they are given a goal and a set of tools, and they decide the steps.
The marketing framing is that agents replace RPA. The honest version is that they solve different problems, and a lot of working RPA should be left alone. Our AI agent service page covers what we build; this is the strategic comparison.
The core difference
RPA follows a script. Every step is defined in advance. Given identical input it does exactly the same thing every time, which is a genuine virtue.
An agent pursues a goal. It reads the situation, picks a tool, interprets the result and decides what to do next. Given the same input twice it will usually — not always — do the same thing.
That difference is the whole story. Determinism is a feature when the process is fixed and a limitation when it is not.
Where RPA still wins
- Fixed, high-volume, rule-based processes. If the steps never vary, RPA is cheaper to run and easier to audit.
- Regulated processes needing determinism. When you must prove the same input always produces the same output, a rule engine is far easier to defend than a model.
- Legacy systems with no API. RPA's screen-scraping origins mean it can drive software that offers no other way in — still its strongest niche.
- Cost at scale. No per-token cost. For millions of identical transactions, rules are dramatically cheaper.
Where agents win
- Unstructured input. Invoices in a hundred layouts, free-text email, scanned documents. RPA needs structure; agents cope with variety. This is the biggest practical difference.
- Judgement steps. Which of nine departments does this belong to? Is this complaint urgent? Rules approximate this badly and the rule set grows until nobody understands it.
- Brittleness. RPA breaks when a screen layout changes, and it breaks silently. Agents working through APIs are more tolerant of change.
- Long-tail exceptions. RPA handles the paths you anticipated. An agent with escalation handles "something unexpected" as a category.
The hybrid that usually wins
The pattern we see work most often: an agent handles the messy front of the process — reading the document, classifying it, extracting the fields — and hands structured data to deterministic automation for the part that must be reliable and auditable.
You get flexibility where the input is unpredictable and determinism where the stakes are high. It is also cheaper than running an agent across the whole process, because the expensive reasoning happens once at the start rather than at every step.
Should you replace working RPA?
Usually not. Migration costs real money and working automation is an asset. Replace it when:
- It breaks constantly because the underlying screens keep changing.
- The rule set has grown to the point where nobody can safely modify it.
- A large share of volume falls into exception queues a person then handles.
- The process now needs to handle unstructured input it was never designed for.
If none of those apply, leave it. "It uses older technology" is not a business case, and any vendor leading with that argument is selling.
Cost comparison, honestly
RPA licences are typically annual and per-bot, and the build cost is comparable to custom automation. Agents cost less to build for messy processes — because you are not writing rules for every variation — but more to run, since each task is several model calls.
The crossover depends on volume and variability. High volume, low variability favours rules. Lower volume, high variability favours agents. Our agent cost guide sets out the running costs properly.
Frequently asked questions
Is RPA obsolete?
No. It is narrower than it was sold as, which is different. For fixed rule-based work against systems with no API, it remains the correct tool.
Can agents drive software with no API?
Increasingly yes, using computer-use capabilities, but it is slower, costlier and less reliable than RPA for the same job. If a process is purely screen-driving with fixed steps, RPA is still the better answer.
Which is easier to audit?
RPA, comfortably. The steps are the specification. Agents need deliberate logging of what they did and why — which is why we build that in as standard rather than as an add-on.
What about accuracy?
RPA is perfectly accurate at doing exactly what it was told, including when what it was told is wrong for this particular case. Agents are less predictable but handle cases nobody anticipated. Measure both against a test set of your real examples rather than trusting either.
Not sure which your process needs? We will look at it and tell you honestly — including when the answer is to leave your existing automation alone. See AI agent pricing or message us on WhatsApp.
