Run your AI agent on a cheaper model, only where it does as well.

Most of the model calls your agent makes don't need your most expensive model. Ergoda finds which ones by measuring each step on your own traffic for parity with the model you use now. A person on your team approves the first route, normally on that replay report; after that, autopilot moves a step only inside a failure limit, and moves it back if that stops holding. Other routers guess which model will do; Ergoda measures it. In the Python SDK, every answer from the cheaper model is also checked against your tool results before it ships.

A step moves only on your own evidence.

Ergoda replays your own traffic on the cheaper model and compares every outcome with your frontier's. A route starts with a person's approval and runs inside a failure limit: 2% added failures at 95% confidence by default, or yours. Other customers' data never moves your traffic.

Where to go next

Get started: measuring in ten minutes, in Python or TypeScript.
Docs: install, the words, then the reference.
Methodology: what a claim means, and what it does not cover.
Pricing: $1 per 1,000 governed requests, and on routed traffic, once a month's holdout is large enough to measure the saving, never more than half of what Ergoda saved you that month, nor below a quarter of list.
agents.md: setup instructions for a coding agent.