Buy the agent for thinking. Build the workflow for running.

The difference is who owns the loop.

Manus is a general autonomous agent. You hand it a goal in plain language. It decomposes that goal into steps, picks its own tools, executes, and hands back a finished artifact. You do not define the steps and you mostly cannot see them coming. That is the product: you are buying the decomposition, not just the execution.

Zapier Agents is a general agent wrapped in an automation platform. You describe what the agent should do in plain English and point it at Zapier's connected app library, which runs to roughly eight thousand apps. It then executes autonomously against those apps. The important structural detail is that you get the outcome rather than the loop: the reasoning happens, but it is not something you assemble or inspect step by step.

n8n is a workflow engine that can contain an agent. You build the run: triggers, nodes, branches, error paths. Since n8n 2.0 shipped in January 2026 it carries native LangChain integration, roughly seventy AI nodes, persistent agent memory across runs, and genuine agent loops where the model calls tools and iterates. The agent is a component inside a workflow you designed, not a replacement for designing one. You can also self-host it, which matters if the data cannot leave your environment.

That ordering, from "the agent owns everything" to "you own everything and rent some intelligence," is the real spectrum. It also predicts the failure modes precisely. The more the agent owns, the less you can inspect when the output is wrong, and the more you are trusting a system that is very good at sounding right.

Vendor marketing converges. Every one of these three will tell you it handles your business processes end to end. The pricing does not converge, and pricing is where a vendor states what it actually expects you to do, because that is the sentence it has to stand behind financially.

Line the three up:

• Manus bills credits per run, which penalises iteration. In our own five-day trial this was one of the four things we marked it down on: the credit model discourages you from re-running a task to refine it. A tool that is expensive to repeat is a tool built for work you do once. That is not a flaw, it is a statement of intent, and it lines up exactly with where Manus was strong for us.
• Zapier bills per task, which penalises autonomy. This is the sharpest one. An agent loop works by calling a tool, looking at the result, and calling another. Every one of those calls is a billable task. So the more genuinely agentic the agent behaves, the more it costs, and the cost is not predictable in advance because the agent decides how many steps it needs. Zapier's per-task model is superb for short deterministic Zaps and structurally awkward for the exact autonomy Zapier Agents sells.
• n8n bills per execution, which makes the loop free inside the run. One workflow run is one execution whether it took three steps or thirty, so an agent that iterates twelve times costs the same as one that got it right immediately. A twelve-step workflow running a thousand times a month consumes roughly twelve thousand Zapier tasks against a thousand n8n executions. That gap is not a rounding difference, and it widens with exactly the behaviour agents are supposed to exhibit.

So the billing tells you the honest positioning. Manus is priced like a consultant you brief once. Zapier is priced like a switchboard, and its agent product is priced against its own grain. n8n is priced like infrastructure you run. Match your task to the pricing shape and you will rarely be wrong, even when the feature lists say all three could do it.

Once you have decided the task repeats, the choice narrows to two and the answer is a sequence rather than a winner.

Start on Zapier Agents when speed of proof matters more than cost. You describe the agent in plain English, point it at an app library of roughly eight thousand connectors, and it runs. For a first experiment, or for a job that fires a handful of times a day against mainstream SaaS tools, that is the correct trade. Do not over-engineer a thing you are not yet sure you want.

Move to n8n when any one of these becomes true. These are the tripwires, and hitting one is enough:

• Volume makes per-task billing bite. The moment your agent loops, the cost stops being predictable on a per-task model and starts being predictable on a per-execution one.
• You need to see and shape the loop. n8n exposes the agent loop itself, with roughly seventy AI nodes and persistent memory across runs. Zapier Agents gives you the outcome. When you need to constrain which tool the agent may call, or what happens on the third retry, you need the loop.
• The data cannot leave your environment. Self-hosting n8n is the only option here of the three, and it removes per-task fees entirely at the cost of running the instance yourself.
• Failure needs to be loud. Building the error path is the whole point of a workflow engine, and it is the capability that separates something you can depend on from something that works most of the time.

We build in n8n and Make for clients, so treat that as disclosed bias and check the reasoning rather than the recommendation. The reasoning is the billing shape and the loop visibility, both of which you can verify against the vendors' own documentation before you take our word for it. We go deeper on the three-way platform question, including where Make fits, in Zapier Agents vs Make AI Agents vs n8n.