Agents that hold 98%+, run after run.
Loamist builds evals and nested evals that make agents work inside your enterprise. Our techniques beat variance, reach your reliability threshold, and hold 98%+ accuracy consistently.
Variance is the problem. Nested evals remove it.
Figure 1 is an illustrative dot-column chart of 40 runs of the same task, on a scale from 65% to 100% accuracy, with a dashed 98% threshold. A run at or above 98% is green. Single agent, an off-the-shelf model: mean 81.2%, plus or minus 11 points run to run. With evals, checks on every output: 90.4%, plus or minus 6 points. With nested evals, evals of the evals: 96.1%, plus or minus 2.6 points. With customer-tuned preferences from the Loamist knowledge base graph: 99.0%, plus or minus 0.5 points, every run above the 98% threshold.
Reliability is measured, not hoped for.
We know how to build a rigorous eval system
We define the threshold a decision has to reach, then build the evals that prove it, before the agent ships.
Multi-step evals are required; we build them
Evals, and evals of those evals, that find and remove variance, not just errors.
In prod, at enterprise scale
98%+ held across repeated runs on live work, not one good result on a good day.