Clarté

/klaʁ.te/

Studying the gap between what agents know and when they act on it.

[!IMPORTANT] This is an experimental research project, not a polished product. The findings are based on 700+ controlled sessions and 30+ experiments, but the real-world evaluation covers a small number of tasks. We’re sharing it early because the results are interesting enough to warrant wider testing. Contributions, replications and skepticism are welcome.

We ran 30+ experiments across 700+ agent sessions to find what measurably changes agent behavior.

First, we measured how agents spend their time. 170 sessions, 7,595 turns:

• 59% of turns: reading files the agent never edits
• 13%: re-running tests with no code change
• 28%: actual work

We assumed the fix was better information. So we built 15 context enrichments: instability metrics, facade maps, API surfaces, type-aware ordering, task-relevant weighting. Each benchmarked in isolation and combination.

**Zero wins.** Not one survived our combinatorial benchmark at realistic temperature. Three optimizations that individually showed -26%, -16% and -32% improvements combined to +63% overhead.

Then we found the placebo.