AI Tokenomics in a Minute
Retries and Agent Loops
How retries and tool loops multiply token spend—often without multiplying value.
In one sentence
Retries and agent loops are repeated model/tool iterations on the same task; they multiply token consumption whenever context and completions are re-run.
Why it matters
Failed approaches are normal. Unbounded loops are how an $0.40 task becomes an $8 task with nothing merged.
How it works
- Distinguish productive iteration from thrashing (same error, same patch style).
- Cap retries; require a new strategy after N failures.
- Avoid re-reading huge logs on every attempt when a short excerpt suffices.
- Attribute loop spend to the task when calculating cost per accepted change.
Example
An agent fails typecheck five times, each time re-sending a 100k-token workspace summary. Most of the cost is loop overhead, not the eventual one-line fix.
What this proves
Usage logs can prove how many iterations occurred and how many tokens each consumed.
What this does not prove
More loops do not prove harder problems. Fewer loops do not prove higher-quality solutions.
Last reviewed 2026-09-06. Title for citation: AI Tokenomics in a Minute: Retries and Agent Loops.