n8n is a workflow automation tool; an AI agent is software that decides what to do next. If your process follows fixed steps, n8n is usually the better and cheaper answer. If the work depends on reading messy input and making a judgement call, you want an agent, and in practice many teams use both together.
What n8n is genuinely good at
n8n lets you connect apps and move data between them by drawing a flow: when this happens, do that, then that. It has a large library of connectors, can be self-hosted, and gives technical teams fine control over each step. For anything that follows a known sequence, it is hard to beat. A new row in a sheet creates a CRM record. A paid invoice posts a message to a channel. A form submission is copied into three systems.
The reason it works so well is that nothing in the flow is uncertain. The same input always produces the same output, you can read the whole flow in a minute, and when it breaks you can see which step failed. That predictability is a feature, and an agent cannot match it. If most of your pain is moving information between tools, start with n8n or a similar tool and do not add an agent to the problem.
Where n8n and similar tools start to strain
Trouble starts when a flow has to understand something rather than copy it. An email arrives and the right action depends on what the customer actually meant. A supplier invoice arrives in a layout you have never seen. A support message could be a complaint, a refund request or a sales question. You can build rules for each case, but the rules multiply, the flow becomes a tangle of branches, and the next unusual input still falls through the gaps.
What an AI agent adds
An AI agent is built around a language model that reads the input, decides which tool to use and takes the next step, then checks the result and carries on. It handles variation: the same request phrased ten different ways, a document with a new layout, a message that needs a sensible reply rather than a template one.
The cost of that flexibility is predictability. An agent can misjudge a case, and it needs guardrails, testing and a clear point at which a person takes over. We explain the practical side in our posts on AI agent guardrails and stopping hallucinations. Used in the wrong place, an agent is slower, costs more per run and is harder to explain than a workflow.
Not sure which part of your process needs an agent?
Send us one workflow. We will separate the mechanical steps from the single judgement call, and tell you honestly if a plain automation already does the job.
Book a Free Workflow ReviewAI agents vs n8n: how to choose
Is the input structured or messy?
Structured input, such as a form with fixed fields or a record in a database, belongs in a workflow tool. Free text, voice, scanned documents and email belong with an agent, because something has to interpret them first.
Is the path fixed or does it depend on context?
If you can write the steps on one page and they rarely change, use a workflow. If the next step depends on what was found in the previous one, and the options are too many to list, an agent earns its place.
What does a wrong answer cost?
A workflow does exactly what it was told, including the wrong thing if the rule was wrong. An agent can be wrong in new ways. For low-stakes work that is acceptable. For payments, legal commitments or anything that reaches a customer unseen, put a human approval step in front of it.
Who will maintain it?
A workflow is maintained by whoever can read the flow. An agent also needs prompt and tool upkeep and regular checks against real cases. Be realistic about who owns that after launch.
Using both: the usual real answer
The strongest setups split the job. The agent handles the one step that needs judgement, such as reading an email and deciding what it is, extracting fields from an invoice, or drafting a reply. Then it hands a clean, structured result to n8n or whichever tool you already run, which does the mechanical work: create the record, update the sheet, send the notification, post to the ERP.
This keeps the unpredictable part small and contained, and keeps the dependable part dependable. It also means you do not have to throw away automations that already work. An agent can start the n8n flow by calling a webhook, and an n8n flow can call an agent for one decision and carry on with its answer. n8n also ships its own AI nodes, so for a simple single-step case you may be able to stay inside the tool you already have.
A worked example
Take a shared support inbox. A workflow tool can already do the plumbing: watch the inbox, create a ticket, assign it, and notify the right channel. What it cannot do well is decide whether a message is a refund request, a delivery question or a sales lead, because customers never phrase these the same way. Put an agent on that one decision. It reads the message, labels it, pulls the order details and drafts a reply for a person to approve. The workflow then carries on exactly as before. Nothing about the plumbing changed, and the single uncertain step now has a reviewer watching it. That is a far smaller and safer project than rebuilding the whole inbox around an agent.
Cost and maintenance, honestly
We do not publish prices and will not guess at yours. The structural picture is clear enough, though. A workflow costs little per run and little to change once built. An agent costs more per run because each decision uses a model, and it needs ongoing evaluation. Neither is free of upkeep: connectors change, APIs get updated, and a flow that nobody owns slowly breaks. Our AI agent cost guide covers the drivers in more detail.
Where we sit in this
Inwizards builds custom AI agents, and we are an independent provider with no stake in n8n. Plenty of the work we are asked about is better solved with a plain automation, and we will say so after looking at the process. Where an agent does fit, we connect it to your systems, including Odoo and your CRM; see Odoo and AI CRM. If your data cannot leave your network, on-premise AI covers running models inside it. For a related comparison, read AI agents vs RPA.
A short checklist before you decide
Write the process as numbered steps. Mark each step either mechanical or judgement. If only one or two steps are judgement, keep the rest in n8n and put an agent on just those. If none are, you do not need an agent. If most are, an agent-led design with a human check at the end is probably right. Start with the smallest version, measure how many cases finish without a person, and expand only once that number is trusted.
Where to start
Pick one process that currently stalls on a person reading something. Run it for a week with the judgement step done by an agent and everything else untouched, with a human approving each result. You will learn more about accuracy, effort and fit from that than from any comparison table, ours included.