When I quote a running cost for an agent project, people expect me to talk about token prices. That's the number everyone watches, and it's usually the smallest line on the bill.
Here's the shape of the last three systems I costed out.
The model calls are predictable
A typical agent run in the systems I build makes somewhere between six and twenty model calls. With a mid-tier model, sensible context management and a prompt cache, that lands in the low single-digit cents per run. Even at a few thousand runs a month, you're talking about a line item that doesn't threaten anyone's budget.
The exception is when something goes wrong in a loop. Which brings me to the actual problem.
Retries are where the money goes
One system I reviewed was spending four figures a month on a workflow that should have cost about eighty dollars. The agent was calling a flaky internal endpoint. When it failed, the tool returned generic error text, and the agent decided a sensible next step was to try again. Sometimes it tried again in a slightly different way. Occasionally it tried eleven times.
Watch out for:
- Retry storms. No cap on attempts, no backoff, no budget check.
- Loop drift. The agent re-plans because it can't tell whether the last step succeeded, so it redoes work it already paid for.
- Re-read everything. Passing the full conversation and all retrieved documents on every call, so the same 40,000 tokens get billed twenty times.
That last one is the most common. Trimming context to what the current step actually needs is normally a 50 to 70% cost reduction with no quality loss, and it's a few hours of work.
The real cost is the human time
Then there's what never appears on the invoice: the person who checks the output.
If your agent produces work that someone has to read carefully before trusting, you haven't removed the task, you've moved it. And reading someone else's work critically is often slower than doing it yourself. I've watched teams celebrate a 70% time saving that didn't exist.
The honest question is not "how much does the agent cost to run?" It's "how much of a human's attention does it still require?" If the answer is "the same as before, but now with a review queue", the project hasn't landed yet.
Ways I've seen that fixed:
- Move the approval gate so it only catches the risky 10%, not every run.
- Make the output verifiable in seconds: cite the exact source line, show a diff, put the numbers in a table instead of prose.
- Route simple cases straight through and hold only the ambiguous ones.
What I do now
Every agent I ship has three ceilings and one report:
- A max steps limit per run, after which it stops and asks.
- A spend cap per run and per day, enforced in code rather than trusted to the prompt.
- A context budget for each step, with retrieval scoped to that step.
- A weekly line in the logs: runs, failures, average steps, average cost, and how many needed a human to intervene.
Those four numbers tell you whether the system is healthy far better than watching the model bill. The token cost is a symptom. Steps, retries and human intervention are the disease.
