𝗜 𝗖𝘂𝘁 𝗠𝘆 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁'𝘀 𝗧𝗼𝗸𝗲𝗻 𝗕𝗶𝗹𝗹 𝗯𝘆 𝟲𝟮% 𝗶𝗻 𝗢𝗻𝗲 𝗪𝗲𝗲𝗸𝗲𝗻𝗱
My AI agent cost $5.40 per task. I reduced that cost to $2.05 per task in one weekend. I achieved this 62% drop without losing quality.
Here is how I did it.
The problem: My agent runs a research loop. It searches the web, scrapes pages, and writes summaries. It was burning tokens in three ways:
- Context stuffing: I sent entire 50,000 character pages to the model. I only needed 2,000 characters. I paid for the whole haystack to find one needle.
- Verbose prompts: My system prompts repeated the same instructions three times. I paid for the model to re-read my own words every time.
- Overusing expensive models: I used high-tier reasoning models for simple tasks like summarizing a single paragraph.
The solutions:
Filter before you send Instead of sending whole pages, I now chunk the text. I find the relevant parts first. Then I send only those parts to the model. This dropped input tokens from 12,500 to 3,200 per page.
Trim the system prompt I deleted redundant instructions. I removed tool descriptions the model already knows. I stopped using boilerplate like "think step-by-step" because modern models do this by default.
Tiered model routing I stopped using one model for everything. I split tasks into three levels:
- Extraction: Use a cheap, small model.
- Synthesis: Use a high-tier reasoning model.
- Formatting: Use a cheap, small model.
The results from a 50-task test:
- Cost per task: $5.40 to $2.05
- Latency: 41s to 28s
- Citation coverage: 67% to 89%
The agent is not smarter. The pipeline is just more efficient.
Three lessons for your production agents:
- Set a hard token budget. Kill the task if it exceeds your limit.
- Cache your results. Do not re-scrape the same URL twice.
- Log everything. You must know exactly which step costs the most money.
Stop reaching for bigger models when quality dips. Start using smaller models with tighter context.
Source: https://dev.to/mrclaw207/i-cut-my-ai-agents-token-bill-by-62-in-one-weekend-heres-the-receipts-1fp1
Optional learning community: https://t.me/GyaanSetuAi