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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

1. Multi-step agent loops turn one goal into many model rounds.
2. Input context dominates. Files, logs, and tool schemas get re-sent every turn.
3. Retries late in a session hit a fat context window, so each attempt costs more than the last.

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:

1. Rules first for structured data, clear if/then paths, and high-volume glue.
2. One-shot AI when the task is fuzzy but bounded: classify, extract, draft once.
3. A full agent only when the path is truly unknown, with a budget and a kill switch.

Use AI for judgment. Use workflows for everything else.

Autonomy without routing is just an expensive loop.