# I traced the agentic calls. Here's where the token consumption comes from

> Source: <https://dev.to/cgougui/i-traced-the-agentic-calls-heres-where-the-token-consumption-comes-from-52nf>
> Published: 2026-09-16 08:12:02+00:00

I expected an agentic coding assistant to use more tokens than a simpler tool. I didn't expect the difference to be this large.

For the exact same simple coding task, I measured:

|  | Pi | Aider | 
|---|---|---|
| LLM calls | 3 | 1 | 
| JSON exchanged | ~760 KB | ~100 KB | 

The task was very simple: a small PyQt project, two files, and a single instruction — **"Make the cards width = total_width / 3."** I already knew exactly which two files needed to be changed.

That made me curious about what was actually happening under the hood.

My API bill had gone above $400/month, despite trying various techniques to reduce token usage. My company doesn't pay for it, so I wanted to understand where all those tokens were going.

I therefore traced the exchanges between a coding harness and the LLM during a simple coding task.

To make the test as clean as possible, I used Pi with no add-ons. I explicitly added the two files that needed editing to its context, then compared the result with the exact same prompt in Aider.

Here is an overview of the Pi workflow:

So you see: for a simple request where I already know what files to edit, there are 3 back and forth with the LLM.

The total size of the json files exchanged is 760kB. Not huge, but again this is a very trivial test. 

The ah-ah moment came when I compared this with the exact same prompt with aider.

Here is the aider workflow:

Here we have a single LLM call. The first prompt is larger, but contains all the necessary context for the LLM to work.

The total size of the json files exchanged is only 100kB !

Of course, the experiment is excessively simple and fits a case where I already know which files need editing. But in my experience, this is actually the case for a majority of my daily tasks. I don't need an agent to "discover" them and burn tokens in a loop. I just need a tool that applies the edits efficiently.

Aider is fantastic for this, but I unfortunately it is not maintained anymore. So I ended up building my own lightweight harness around this exact single-prompt philosophy called Frugaast.

By switching part of my workflow away from agentic loops and just using single-shot edits for known files, my API bill dropped from $400+ to under $100 a month.

Has anyone else traced their AI editor's network calls? I'm curious if other popular tools like Cursor or Copilot are hiding massive token bloat under the hood.