We Cut Our AI Agent API Spend by 79% — Here's the Exact Fix
Source: Dev.to
Problem
We were spending $198/month on AI agent API costs. The majority of the spend wasn’t coming from the LLM calls we expected, but from a forgotten agent loop that ran every 60 seconds and invoked four external tools each cycle.
- 5,760 tool calls per day
- 172,800 tool calls per month
When multiple AI agents run together, their API costs pool together, making it impossible to answer basic questions without attribution:
- Which agent is burning the most tokens?
- Which tool calls are expensive vs. cheap?
- Which loop cadence is appropriate vs. excessive?
We had no answers, so we built a cost‑attribution pattern into every agent loop.
Solution
Step 1: Tag every LLM call with an agent ID
Add metadata to each API request, for example:
{
"agent": "suki",
"loop": "content-loop",
"task": "draft-tweet"
}Log this metadata to a daily cost file so you can later aggregate spend by agent, loop, and task.
Step 2: Set loop cadence intentionally
Don’t run a loop every 60 seconds unless you truly need that level of responsiveness.
- Our content loop now runs every few minutes.
- Our ops loop runs on events rather than on a timer.
Step 3: Weekly cost review
Each week, pull a cost‑by‑agent breakdown. If an agent’s cost spikes, investigate immediately before the expense compounds.
Results
- $198/month → $42/month
- 79 % reduction in API spend
We achieved this without switching to a cheaper model or cutting agent functionality—simply by stopping work that no one asked for.
Resources
The complete cost‑attribution configuration—including the logging structure, review checklist, and loop‑cadence decision framework—is available in the library:
If you’re running AI agents and haven’t audited your loop costs, this is likely the highest‑ROI hour you’ll spend this week.