Earlier this week I published a worked cost model for running AI agents in production on behalf of Control Plane, where I work. Tokens, sandboxes, NAT gateways, storage, all of it, with every input written down so anyone could check the math.
Someone did. It wasn't a person.
The first comment came from Aisling, an account whose bio says it's an AI agent that checks published claims against their sources. It recomputed every line in the post. Then it found the one line where I'd been wrong, and it was a big one.
The model
The setup was 300,000 agent tasks a month, 8 model calls per task, about 6,000 input tokens per call. 4,500 of those tokens were a shared prefix: system prompt, tool definitions, standing context. Prices were Claude Sonnet 5.5 list prices:
| Price per million tokens | |
|---|---|
| Input | $2.00 |
| 5-minute cache write | $2.50 |
| Cache read | $0.10 |
| Output | $10.00 |
I assumed the first call in every task writes the prefix to the cache and the other 7 read it. That gave me a cache-write line of 1.35 billion tokens, or $3,375 a month, the third-biggest token line in the model. Total: $21,919 a month, $0.073 per task.
Correction 1: the cache never goes cold
Aisling's point: at 10,000 tasks a day, a new task starts every 8.6 seconds. The cache lives for 5 minutes. With a shared prefix and steady traffic, it basically never expires, so the first call of almost every task is a read, not a write.
Its version: one write per 5-minute window, 8,640 writes a month, 38.9 million write tokens, $97 instead of $3,375. Monthly total down to about $18,641, or $0.062 per task.
It was right. I'd even written in my own assumptions that the shared prefix made my numbers conservative. I just hadn't followed that sentence to its conclusion.
My add-back
One thing it dropped: those 1.31 billion tokens don't disappear. They stop being writes and become reads, which still cost $0.10 per million. That's about $131 back (I said ~$135 in the thread), so roughly $0.063 per task.
Correction 2: it fixed its own math
Its reply agreed and then reran the whole thing more carefully than I had:
- Reads: 9.45B tokens → 10.761B, $945 → $1,076.10
- Writes: 1.35B tokens → 38.88M, $3,375 → $97.20
- Total: $18,772.00 a month, $0.0626 per task
Then it flagged one term it hadn't priced because it couldn't verify it: does a cache hit refresh the 5-minute TTL? If it does, writes don't happen once per window. They only happen after a real gap.
The last piece
It does. Anthropic's prompt caching docs say the cache "is refreshed for no additional cost each time the cached content is used." With 8.6 seconds between tasks, the cache only goes cold after a real lull, overnight or during a deploy.
So the 8,640 writes were an upper bound. Push writes toward zero and the floor lands around $18,679 a month, about $0.0623 per task.
Final answer: $0.062 to $0.073 per task, and the term that moves it is how often your traffic leaves a gap longer than the cache TTL.
What I took away
About the math. At steady volume, cache writes are about traffic gaps, not task count. That's easy to miss because the docs price writes per request, and it's natural to model them per request. I wrote up the full mechanics, including a small simulator of writes versus traffic level and when the 1-hour TTL beats the 5-minute one, on Hashnode.
About being reviewed by an agent. It was a little uncomfortable and mostly great. It showed its work line by line, it said which lines it checked, it corrected itself as readily as it corrected me, and it said plainly what it hadn't verified instead of guessing. The number got smaller and more honest both times. Most human reviews I've had did less.
It also ended its first comment with a price list for its services. So, you know, still the internet.
Over to you
Has anyone else had an agent fact-check their published work? Did it catch something real, or was it noise?
And for those running agents in production: are you modeling cache writes per request or per traffic gap? I'm curious how far off most people's estimates are.
