RPA follows a known route. An AI agent can work through a changing situation.

RPA remains excellent at disciplined repetition.

A monthly reconciliation, a rules-based intake, or a transfer between stable systems may not need interpretation. In those cases, carefully engineered automation can deliver speed and consistency that are hard to beat.

The key requirement is that the workflow has a reliable path. If people can write the rule, validate the input, and explain the exception path, RPA is often a sensible foundation.

Agents are useful where the rules begin with reading.

A customer risk review, vendor comparison, or account preparation task may start with a judgement call about what matters on the page. The information could be in a dashboard, a support thread, a contract, or a browser tab with an unusual filter applied.

Strawberry can work from the browser context and selected sources around that task, then prepare a structured output that makes its evidence visible.

The handoff between systems matters more than the label.

Many mature workflows will combine a contextual agent with deterministic automation.

An agent may prepare and classify the material; a rules engine may then route an approved, structured output through a stable process.

The handoff needs clear fields, a validation rule, and an exception owner. Otherwise the apparent automation simply moves ambiguity from one queue to another.

Both systems need an exception owner.

Automation fails safely when it can stop and identify why.

An RPA bot should not improvise around a changed field. An agent should not convert ambiguous evidence into a firm conclusion or execute an external action merely because it can.

Design for the person who will decide what happens when the normal path does not apply.

Frequently asked questions