Use an integration platform such as Zapier or Microsoft Power Automate when the data is already structured and every app involved has a connector or API. Use RPA when the only way into a system is its screens. Use an AI agent, with a person approving what matters, when the input is messy (like emailed PDFs in many layouts) or the next step depends on context.
What integration platforms like Zapier and Power Automate do
Integration platforms connect apps through their APIs with a trigger and one or more actions. In Zapier, a Zap is exactly that: when a form is submitted, add a row to a sheet. Zapier lists integrations for more than 9,000 apps, and filters and paths add if/then rules. In Microsoft Power Automate, cloud flows can start when an event happens (an email arrives, for example), on a schedule or when someone presses a button.
Both now include AI. Zapier’s AI by Zapier step adds a model to a Zap to summarize, classify or extract data. When you give that step tools, it can reason and act on its own, and Zapier says this is now where its former standalone Agents run. Power Automate can draft a flow with Copilot from a plain-English description, and its AI Builder actions can extract information from documents and invoices.
Where they fit: structured data moving between apps with connectors, simple rules you can state in a sentence and flows a technical person on your team can build and own.
Where they struggle: inputs that change shape every time, since each new layout means another rule or prompt to test. Errors stop a run unless you build an error path, and Zapier turns a Zap off if it errors repeatedly.
What RPA tools like UiPath and Power Automate Desktop do
RPA runs software robots that work an application’s screens the way a person would. A UiPath Robot is designed to interact with applications like a human user, either attended (started by a person on their own machine) or unattended (running on its own, often on a separate machine). Power Automate’s desktop flows do the same, including for legacy applications such as terminal emulators, and find what to click by UI elements, images or screen coordinates.
That screen-level approach is also the weak point. UiPath’s selector documentation explains that a robot finds each button or field by stored attributes, and that apps with changing layouts or attribute values can break those selectors, so you may have to fix them by hand. UiPath now offers AI-based semantic selectors, designed to keep automations working when a UI changes.
Where it fits: older systems with no API and high-volume steps on screens that rarely change.
Where it struggles: unstructured inputs, exceptions nobody scripted and upkeep that grows with every robot.
What an AI agent adds
An AI agent uses a language model to read unstructured input, decide the next step and use tools to act or draft. UiPath’s agent documentation lists the parts: a prompt that sets the role and limits, context to ground decisions, tools that take actions and escalation paths where a person reviews or approves.
That makes agents a fit for messy inputs, like purchase orders in fifty layouts, and for work where the right action depends on context, such as checking an account before sending a reminder. The same UiPath page is candid about limits: it lists high-risk financial transactions and zero-error regulatory work as poor uses, and says tasks with high accuracy, legal, financial or regulatory constraints should stay with deterministic automation.
Agents also bring a new risk. Because they read content from outside your company, a hidden instruction in an email or PDF can try to steer them. OWASP ranks this prompt injection risk first in its Top 10 for LLM applications. Give an agent narrow access and have it draft rather than act on anything important. More in is it safe to give an AI agent access to your ERP?
Side by side
| Question | Integration platform | RPA | AI agent |
|---|---|---|---|
| Best input | Structured app data | Fixed screens | Emails, PDFs, varied files |
| How it reaches systems | APIs and connectors | The screen | Tools: APIs or automations |
| Exceptions | Stop without an error path | Stop unless scripted | Can go to a person |
| Goes wrong when | An app or field changes | A screen changes | Vague rules or loose access |
| Upkeep | Fixes when apps change | Grows with each bot | Reviewing exceptions and logs |
One order, three ways
Take one emailed purchase order that has to become a sales order in your ERP.
- An integration platform can catch the email and pass the attachment on. An AI step can extract the fields, but each customer’s layout needs testing, and matching their part numbers to yours still needs rules.
- RPA can type the order into the ERP screen once someone has read it, but reading is the hard part.
- An AI agent can read the PO, match the customer and items, check prices and draft the sales order, sending anything unusual to a person.
That makes order entry a good first workflow to test an agent on: the input is messy, the rules are clear and every draft can be checked before it counts. See order entry automation for what that looks like.
How to choose for one workflow
Ask these questions about the single workflow you want to automate:
- Is the input structured, like form fields or app records, or free-form, like PDFs and emails? Structured input points to an integration platform.
- Does every system involved have an API or a connector? If one doesn’t, use RPA for that step.
- How many variations are there? If covering them would take dozens of rules or templates, consider an agent.
- How often are there exceptions, and who handles them today? Whatever you build needs a path to that person.
- What does a mistake cost? If it’s a wrong payment or a compliance problem, keep that step deterministic and add an approval.
- Who will maintain it when an app, a screen or a customer’s layout changes?
Costs and maintenance
Compare running costs and upkeep, not just licenses.
- Integration platforms charge by usage or by user. Zapier counts a task each time an action completes, and an AI by Zapier step uses 3 or 5 tasks per run on its stronger model tiers (on paid plans, new steps default to the 5-task tier). Power Automate has user and capacity licenses, and Microsoft 365 plans include standard connectors only, so premium or custom connectors need an extra Power Automate license. See the handoffs worth automating first between Microsoft 365, QuickBooks and NetSuite for where that matters.
- RPA licensing depends on how robots run. In Power Automate, the Premium user license includes attended desktop flows, while a Process license turns a machine into an unattended bot that runs one desktop flow at a time. Upkeep grows with every bot, because each one depends on screens that can change.
- AI agents add model usage and review time: someone handles exceptions and spot-checks drafts. Ask how usage is priced, since an agent reads every email and attachment it’s given.
For example, with illustrative numbers: a Zap with a trigger, one AI step on the 5-task tier (with no tool calls) and two regular actions uses 5 + 2 = 7 tasks per run. At 500 orders a month, that’s 3,500 tasks.
When to combine them
You don’t have to pick one tool for a whole workflow. Use the simplest tool that works for each step:
- An integration platform watches the order inbox and files each attachment.
- An agent reads the PO and drafts the order, with exceptions going to a person.
- RPA keys the order into an older system with no API, if you still run one.
- The integration platform posts a message in Teams when a draft is waiting.
As the AI features above show, the lines between these tools are blurring, so choose by the step, not the brand.
A rule of thumb
- If the data is structured and the apps have connectors, use an integration platform.
- If the only way in is the screen, use RPA and budget for upkeep.
- If the input is messy or needs judgment, use an agent that drafts, with a person on the exceptions.
Kestrel is an AI-native consulting firm in St. Louis. The agent we deploy is designed to find the repetitive steps between your tools, including messy-input work like order entry from emailed POs, and to draft automations your team reviews and switches on; nothing runs until you do. It’s in development, and early access is open.