token budget is becoming part of my agent workflow design

Reddit r/artificial / 5/3/2026

💬 OpinionIdeas & Deep AnalysisTools & Practical Usage

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Key Points

The article argues that token budget is increasingly a core design constraint in agent workflows, influencing how people structure tasks.
It warns that if runs feel too expensive, teams tend to under-test, shorten experimentation, and avoid repetitions needed to uncover failure modes.
It also cautions that if runs feel too cheap, teams may over-delegate to agents, generating more output than they can realistically review.
Instead of asking which model is best overall, it proposes assigning different model “levels” to different workflow steps based on clarity and required judgment.
The suggested rule is to use cheaper/lower-reasoning models for bounded repetitive work, stronger models for ambiguous or hard-to-judge steps (including debugging), and human review for final acceptance.

I think token budget is becoming part of agent workflow design.

If every run feels expensive, people under-test. They save quota, overthink prompts, and avoid the repetition that reveals failure modes.

If every run feels cheap, people can over-delegate. They generate more output than they can review.

So the useful question is not "which model is best?"

It is:

Which step deserves which level of model?

My current rule: