AI agents vs. RPA.
RPA bots click through fixed UIs. AI agents reason about goals and decide what to do. One pattern handles the deterministic 80%; the other handles the messy 20% that used to fall back to a human.
RPA is great
when the inputs never change.
Stable, structured inputs
Same form, same fields, same schema. RPA replays the same sequence every time without surprise.
Legacy systems
Mainframes, terminal apps, SAP screens with no API. RPA's screen-scraping is the only path.
High-volume, low-judgment
Move data from A to B at scale. As long as the rules do not bend, RPA bots are cheap and fast.
The five failure modes every RPA team knows.
Unstructured input
A free-form email, a PDF with shifted fields, a chat message. RPA cannot parse intent — agents can.
UI changes
A button moves, a label changes. RPA bots break silently. Agents adapt because they reason about the goal, not the pixels.
Exception handling
An expected field is empty. RPA throws. The agent decides whether to retry, ask, or escalate.
Cross-system reasoning
Pull from CRM, billing, and a doc to make a decision. RPA cannot synthesize; agents do it as their default behavior.
Maintenance cost
Every RPA flow is one UI change away from breaking. Agents do not need re-recording when the surface shifts.
Output requiring tone
A reply, a summary, a draft. RPA cannot produce; agents do this natively.
Move from RPA without ripping it out.
1. Keep RPA where it works
Stable, deterministic UI workflows stay on RPA. No reason to migrate them.
2. Pick the breakage cases
Find the workflows that fail back to humans. Those are the agent candidates.
3. Layer in agents
Agents handle the messy paths and call into RPA flows when the deterministic step is needed. See full comparison →
See an agent handle the edge cases.
Bring the RPA flow that keeps falling back to a human queue. We'll show you an agent take it.