𝗔𝗴𝗲𝗻𝘁 𝗟𝗼𝗼𝗽 𝗖𝗼𝘀𝘁: 𝟭𝟭𝘅 𝗬𝗼𝘂𝗿 𝗣𝗲𝗿-𝗖𝗮𝗹𝗹 𝗤𝘂𝗼𝘁𝗲
Most teams budget for AI agents by looking at the cost of a single call. They see a price per million tokens and estimate the cost of one action. This approach is wrong.
An agent loop does not cost the same as one call. It costs much more.
The reason is simple. Every time an agent makes a tool call, it resends the entire context. You pay for the system prompt again. You pay for every tool description again. You pay for the entire history of the conversation again.
As the loop continues, the payload grows. The cost grows quadratically. You do not pay for a single call. You pay for the area under a rising curve.
I built a tool to measure this. It is a 40-line Python script called loop_forecast.py. It is offline, keyless, and reads your JSONL traces. It tells you the real cost before you ship your code.
On my test run, the results were clear:
- Quoted cost per invocation: $0.20
- Measured effective cost per task: $2.26
- The gap: 11.29x
If your loop is short and simple, the tool will show a small gap. If your loop is heavy, it will show a massive breach. You can use this as a CI gate. If the cost gap is too high, the build fails.
Stop budgeting based on a single inference. Use your own traces to find your real number.
Key features:
- No network access or API keys required.
- Uses tiktoken for accurate counts.
- Provides a curvature metric (k) to show how costs scale.
- Deterministic output for reliable CI/CD.
The mistake is unit confusion. A task is not one call. A task is N calls, each with a growing tax.
Source: https://dev.to/alex_spinov/agent-loop-cost-11x-your-per-call-quote-in-40-lines-5dfn
Optional learning community: https://t.me/GyaanSetuAi