AI Agents vs Zapier and Make, Which Do You Actually Need?

Pros

• Predictable. The same input produces the same output on every run, which makes testing and sign-off straightforward.
• Flat cost of running. You pay a subscription with a task or operation allowance, so the bill is forecastable from volume alone.
• Fast to build and to change. An operations person can often build and maintain a flow without booking engineering time.
• Auditable by default. The path is visible on screen and the run history shows each step with the data it handled.

Cons

• Breaks on variation. Every new case needs another branch, and branch counts grow faster than teams expect.
• Weak on unstructured input. Free-text emails, scanned invoices and the same request phrased five ways are exactly what a fixed path is bad at.
• Silent drift. When an upstream form field or an API response changes shape, the flow often keeps running happily against the wrong data.
• Large scenarios become their own maintenance problem, with business logic buried in a visual editor rather than held in version control.

Pros

• Handles inputs nobody can enumerate in advance: free text, inconsistent documents, the reference that turns up in a different field each time.
• Absorbs the awkward minority of cases that would otherwise need a branch each, so the workflow survives contact with real inputs.
• Turns unstructured content into structured fields: email threads, PDFs and transcripts become records your systems can use.
• Extending scope means giving the agent another tool, rather than redrawing the whole decision tree by hand.

Cons

• Cost of running scales with volume. You pay per request, so model cost is a growing line item rather than a flat subscription, and it is the number most often missing from a business case.
• Non-deterministic. The same input can produce a different answer, so testing needs an evaluation set of real historical cases rather than one successful run.
• Most of the build is ordinary engineering: integrations, permissions, retries, idempotency, escalation and logging. The model is the small part.
• Failure is quiet and plausible. A wrong answer usually looks reasonable, which is why human review above a defined risk threshold is not optional.
• It needs maintaining. Models get deprecated, replacements behave differently on your edge cases, and prompts need regression testing the way code does.