RPA vs AI Agents vs Workflow Automation: Which One Fits Your Process?

Three ways to automate a process

Each approach answers a different question. RPA asks "how do we operate this screen without a person?" Workflow automation asks "how do we move data between these systems when something happens?" An AI agent asks "how do we finish this task when the steps depend on what we find?" Picking the wrong one is the most common reason automation projects stall.

RPA: the bridge to systems without APIs

RPA earns its place when a process runs through software you cannot change and that has no usable API: an old desktop ERP, a mainframe terminal, a government portal, a supplier website. The bot logs in and does what a clerk would. Microsoft lists Power Automate Premium at $15 per user per month, the Process plan for unattended RPA at $150 per bot per month and Hosted Process at $215 per bot per month, all paid yearly (checked October 2026). UiPath lists a free Community plan and a Basic plan from $25 a month, with Standard and Enterprise plans priced through sales.

The cost people underestimate is maintenance. Every change to the target screens, a browser update or a new login prompt can stop a bot. If you own the system the bot is driving, building an API is often cheaper over three years than keeping bots alive. Our legacy modernization work frequently starts with exactly that trade-off.

Workflow automation: the default for API-connected work

If every system in the process has an API, a workflow tool is usually the fastest route. Triggers (a form submitted, a row added, an email received) start a sequence of steps that move and transform data. n8n, Make and Zapier differ mostly in how they bill, how they handle code and whether you can self-host; our n8n vs Make vs Zapier comparison covers prices and licensing so we will not repeat it here.

All three can now call a model as a step: classify this email, summarise this document, extract these fields. That covers a large share of what people call "AI automation". The limit arrives when the flow needs many branches, proper error handling, versioned changes, tests, or volumes where per-task pricing climbs. At that point the flow moves into code.

Custom AI agents: when the steps are not known in advance

An agent fits when the work varies case by case and needs judgement: an inbound request that might need a lookup in two systems or five; an invoice exception that needs a policy check and a supplier email; a research task. The agent chooses its next step from a set of tools you define, so the engineering is in the tools, the limits and the tests. Agentic AI vs AI agents vs generative AI explains the definitions and the controls we put around agents.

Agents are the most flexible option and the least predictable. Each extra model call adds cost and a chance of error, and each action needs a permission, a limit and a log. Do not use an agent for a process with fixed steps; a workflow does it cheaper and more reliably.

Decision table

Mixing them is normal

Real processes cross categories. A typical accounts-payable flow we would design: emails arrive and a workflow tool saves attachments; an extraction pipeline reads the invoices into fields with a model; rules match them to purchase orders; exceptions go to an agent that checks the supplier record and drafts a query to the supplier for a person to approve; and if the ERP has no API, an RPA bot keys the approved invoice in. Each part uses the cheapest tool that is reliable for it.

On Edge OCR the extraction part of such a pipeline runs on AWS Lambda with AWS Bedrock for document understanding, behind a three-queue Redis design: processing, in-flight to stop duplicate work, and a dead-letter queue so failed documents are kept for retry rather than lost. That queue discipline is what separates production automation from a demo, whichever tool sits on top.

Signs you picked the wrong tool

• RPA bots break after most releases of the target system: you are scraping screens that change; ask for an API or move that step.
• A Zapier or Make scenario has grown past 40 steps and nobody wants to touch it: it has become software, so give it tests and version control in code.
• The monthly task or operation bill rises faster than volume: per-step pricing is charging you for loops and retries.
• An agent takes the same path on 95 percent of cases: replace it with a workflow and keep the agent for the exceptions.
• Nobody can say why a case went the way it did: add step-level logs before adding features.

Cost comparison

For payback maths and model fee examples, read AI automation cost. To model token costs for an agent, use the AI agent cost calculator.

How we run an automation project

1. List candidate processes with runs per month, minutes per run and error cost. Pick one with high volume and low risk.
2. Map the systems: which have APIs, which need RPA, where unstructured input arrives.
3. Build the pilot with the simplest tool that works, plus logging and an error queue from day one.
4. Measure for four weeks against the baseline, then decide what to extend, rebuild in code, or stop.

Our AI automation page covers how we scope these projects, and the generative AI development hub covers the model side. For a support-specific version of the same decision, see generative AI for customer service.

Sources

• Microsoft Power Automate pricing: https://www.microsoft.com/en-us/power-platform/products/power-automate/pricing (checked October 2026)
• UiPath pricing: https://www.uipath.com/pricing (checked October 2026)
• Anthropic engineering: Building effective agents: https://www.anthropic.com/engineering/building-effective-agents