Agentic Is Powerful. The Bill Is in the Tokens.

Agentic workflows drive up costs due to excessive token usage, as each action triggers multiple model calls and context grows with every turn. n8n recommends using AI only for judgment tasks and workflows for deterministic work to avoid 'token addiction.'

Agentic workflows are useful. The problem is how fast tokens pile up. A single do this can turn into dozens of model calls. Context grows every turn. Retries get more expensive late in the session. Even simple search and re-read loops show up on the bill. That is token addiction: wrapping deterministic work in an LLM every time. Why agentic cost escalates What token addiction looks like Agent greps the same repo on every run LLM summarizes JSON that a Set node or script could map Full HTML dumped into context instead of structured fields No max steps on tool loops You are paying for judgment on work that never needed judgment. A simple control plane With a clear process, n8n helps you decide when AI should run: Use AI for judgment. Use workflows for everything else. Autonomy without routing is just an expensive loop.