Most agent tooling stops at the classic Observe - Evaluate loop. You can stop, pause, insert a human into your flows. But it mostly happens in development. Once agents hit production they're on their own, and engineers lean on Sentry or PostHog to find out something broke. Then improvement is you, by hand: digging through traces, tuning a prompt, redeploying, hoping.
Most agents pass their evals and fail in production.
Prefactor is the evaluation layer that closes the gap.
We score every agent run in real time, surface quality regressions and drift as they happen, and show engineering teams exactly how their agents are performing at scale.
Built for the teams shipping agents to customers.
We have had a lot of fun since the PH launch and making it to number 1. Such a great experience and everyone who got involved and helped us I am insanely grateful to.
As we move on from PH into helping teams evaluate their agents in real time, I'm looking to understand how everyone is doing it. Are you using static evals and running a hit and hope approach? Are you using off the shelf agent evals from OSS? Are you using a tool?
I'd love to know so we can improve what we have to make it the world class solution you don't have to think twice about when building your agents.
Ahead of Product Hunt we have open sourced Prefactor Evals, No LLM Required. Apache 2.0, public repo.
Most agent failure is behavioural: a silent loop, a step that failed, a run that never finished, a task that took four times the usual work. All of that is checkable in code, deterministically, for nothing. There is no model client anywhere in the project and there never will be, so it cannot cost you a token by construction.
