AI Agents vs RPA: Which Automation Is Right for You?

RPA is automation by imitation. The bot follows the same steps a human would follow in a UI: open an application, click fields, copy/paste values, and submit forms.

In practice, RPA is strongest when:

• The workflow is stable and rarely changes


• Inputs are structured (fixed CSV columns, consistent forms)


• The business rules can be expressed as if/then logic

Market reports put the RPA market around $3.79B in 2024 with high growth (often cited around 43.9% CAGR in some reports). Whether the exact number is 3 or 5 billion, the point is clear: RPA exists because many business processes are repetitive and expensive to do manually.

In Dutch operations, we often see RPA used for invoice data entry, HR onboarding checklists, and copying data between ERP and CRM systems.

Under the hood, most RPA bots depend on selectors and screen structure. They might combine UI clicks with OCR, regular expressions, and simple rules. This works, but you pay for it in maintenance. Common failure modes we see in the field:

• A UI redesign changes element IDs or tab order


• A portal introduces MFA, CAPTCHAs, or a new step


• A process depends on timing (page load, network delay)


• An upstream spreadsheet column changes and everything shifts

RPA is often the right choice when you have a well-defined checklist and you can test it end-to-end. For example: exporting a daily report, updating a status field, or copying values from a known portal into a known internal form. In those cases, the determinism is a feature: you can predict exactly what the bot will do and you can write straightforward monitoring around it.

An AI agent is a goal-directed system that can perceive input, decide what to do, and execute actions via tools. In other words: it doesn’t just repeat a recorded script—it adapts to the case.

Agents are strongest when:

• Inputs are unstructured (emails, PDFs, natural language)


• The workflow has exceptions and branching paths


• You need to combine rules + context (customer tier, contract terms, VAT posture)

This is also why agents demand more governance than RPA. RPA mostly fails by doing nothing (bot breaks). An agent can fail by doing the wrong thing (wrong update, wrong email, wrong classification). That’s why guardrails and approvals are non-negotiable in production deployments.

For Dutch businesses, the practical benefit is handling the messy reality: invoices that look different per supplier, customers who write in mixed Dutch/English, and processes that jump between email, Exact/AFAS, and a helpdesk.

A key difference: agents can work with intent and state. Instead of “click here, then here”, the agent can say “the goal is to update the invoice status to paid; the required fields are X and Y; fetch them from system A; write them to system B.”

In production, we don’t let the model free-write actions. We constrain it: structured outputs, validated parameters, and explicit tool permissions. For example: read-only access by default, write access only for reversible updates, and approvals for anything that moves money or changes customer accounts.

This is why agent deployments look like engineering projects: you’re building safe automation, not just a clever interface.

We describe this as bounded autonomy. The agent is allowed to do the routine steps automatically, but the boundary is explicit: when confidence drops or risk rises, it escalates. That boundary is what makes an agent acceptable in finance, customer service, and operations.

Here’s a 10-point matrix we use to keep the conversation grounded.

If you remember one row, make it the error profile. RPA needs resilience engineering. Agents need safety engineering.

Two practical notes:

• If your process is 100% structured and stable, adding an LLM rarely helps. RPA or a direct integration is cleaner.


• If your process is exception-heavy, RPA tends to create a hidden “bot babysitting” job. Agents reduce that by handling variation and escalating only when necessary.

Let’s compare three workflows where Dutch businesses commonly consider automation.

1) Invoice processing

• RPA approach: read a fixed PDF template or portal export, copy fields into Exact/AFAS, upload the attachment. Works well when suppliers and formats are stable.


• Agent approach: ingest any invoice PDF, extract supplier + VAT breakdown, match to PO or contract rules, flag anomalies, and post the booking with evidence links. Works better when formats vary and exceptions matter.

• RPA approach: route based on keywords and send templates. Good for acknowledgements and very strict flows.


• Agent approach: read the thread, fetch context (order, contract, previous tickets), draft a reply in your tone, and propose next steps. Better when context changes per customer.

• RPA approach: move data between CRM and ERP, create accounts, send standard emails.


• Agent approach: validate that required documents exist, detect missing information, ask the right follow-up questions, and only execute the setup once the checklist is complete.

To make this concrete, here is a Dutch VAT-flavoured invoice example. Suppose a supplier invoice includes €1,000 net at 21% VAT. Output is not relevant here; input VAT is €210. A bot can copy “€210” into a field if the PDF layout is stable. An agent can also validate the arithmetic (gross should be €1,210), detect a missing VAT number, and flag anomalies (duplicate invoice number, unusual bank account).

For email workflows, the difference is context. RPA can send a template. An agent can read the thread, see that the customer already provided the order number, fetch the delivery scan, and draft a reply that matches the situation (“delivered to neighbour”, “returned to sender”, “delivery attempt failed”).

For onboarding, agents are useful because they can enforce checklists. They can verify that required documents exist, request missing fields (KVK number, invoice address), and only execute account creation once the checklist is complete.

In practice, the agent model tends to create an exception queue instead of a backlog. Instead of waiting until month-end, you resolve a handful of ambiguous items each week. That shifts the work from “deadline panic” to a small, steady review habit.

RPA is enough when the process is predictable and you can define the rules without hand-waving. If the workflow breaks often because the UI changes, fix the process or use APIs—don’t keep patching clicks.

You need AI agents when:

• Inputs vary a lot (emails, PDFs, multilingual messages)


• Exceptions are common and expensive


• The workflow spans multiple systems and requires context

For Dutch teams, also consider compliance. If the workflow touches personal data or money, agent deployments should include least-privilege permissions, audit logs, and approvals for irreversible actions.

We also ask one diagnostic question: where does the truth live? If the truth lives in a database/API, connect to it. If the truth lives in screenshots and inconsistent PDFs, you need interpretation—which is where agents help.

One caution: don’t automate a broken process. If humans disagree about the rules, an agent will surface that disagreement quickly. Use the rollout to clarify policy and define exceptions.

A quick checklist before you pick a direction:

• Can you define success with a measurable output (a status changed, a record created)?


• Do you have an API you can call, or are you forced into UI clicking?


• How often do exceptions happen, and what do they cost in human time?


• What is the worst-case failure (wrong email, wrong booking, wrong payment), and how will you prevent it?

We don’t see RPA disappearing. We see it becoming a tool inside a larger system.

One practical future model is: an AI agent makes the decision, then triggers RPA bots for the mechanical steps that still require UI automation. This matches what several RPA vendors describe: treat RPA as the “hands” and AI as the “brain.”

Market analysts sometimes frame this as a large reshaping of automation spend (you’ll see forecasts like a $58B shake-up by 2027 across RPA and intelligent automation). The exact forecast is less important than the operational trend: teams want automation that can handle exceptions without building a thousand brittle scripts.

In our implementations, this looks like: an agent reads an email request, decides what system updates are needed, and then either calls APIs directly or triggers an RPA bot as a fallback when no API exists.

We see three futures happening in parallel:

1) RPA stays as “hands” for legacy UIs where APIs don’t exist.
2) Agents become the orchestration layer: read inbound work, decide the path, trigger tools/bots, verify outcomes.
3) More systems expose APIs, and both RPA and agents shift toward API-first automation.

For Dutch businesses, the practical takeaway is to invest in the integration layer and observability. That’s what makes automation durable when tools and UIs change.

If you already have RPA, you don’t need to rip it out. Migrate in layers:

1) Instrument and stabilise: measure failure points and fix the worst brittle steps.
2) Add a decision layer: let an agent classify requests and pick which bot/workflow to run.
3) Replace UI automation with APIs where possible.
4) Add guardrails and approvals for high-impact actions.

The goal is incremental improvement: fewer broken bots, fewer manual exceptions, and better auditability.

A migration detail that saves pain: start by wrapping existing bots with monitoring and a clear “stop/rollback” policy. Then add the agent as a supervisor that decides when to run a bot, when to ask for human approval, and when to fall back because the UI changed.

A five-step migration sequence that works well:

• Step 1: list your bots and classify them by stability (how often they break).


• Step 2: wrap bots with logging and clear alerts.


• Step 3: add the agent as a router (which bot should run, what inputs are missing).


• Step 4: let the agent handle the exceptions humans currently resolve manually.


• Step 5: replace the most brittle UI steps with APIs over time.