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The honest answer to n8n vs make vs zapier is that all three will run your first workflow fine. The choice only starts to matter around the point where you have thirty of them, one has been failing quietly for a fortnight, and the invoice has stopped looking reasonable. Picking on features is how teams end up migrating a year later. We build automation for clients on all three, and we also maintain our own. The differences that have actually cost people money are pricing model, what happens when a run fails, and how hard it is to leave. The

Keeping ai agents reliable in production is a different discipline from getting one working. A demo agent has a person watching it. A production agent runs at three in the morning against inputs nobody anticipated, and the first sign of trouble is usually a number in a billing dashboard or a customer asking why they got a strange email. We run agents unattended, including an internal platform that produces video end to end without a human in the loop. Everything below is a failure we have actually had, and the control that stopped it happening twice. Reliability Is Not Accuracy

Every ai agent frameworks comparison you find online scores the same boxes: memory, tool calling, multi-agent support, streaming. All of them tick nearly all of them. That is why the comparisons are useless. The frameworks converged on features two years ago, and the thing that separates them now is what they take away from you when something breaks at 2am. We build and run agents for clients, and we also run our own. Our internal video production platform takes a topic from brainstorm through scripting, scene planning, clip selection, voiceover and assembly without a person in the loop. That system