At Confident AI, we're building the quality layer for AI applications: the place where teams evaluate what they build, trace what it does in production, and decide what to improve next. Today, teams send us millions of traces from the agents they run in production.
Now we're helping them make sense of all of it. Confident AI Monitor is our new way to watch production: it reads your traffic as it arrives, turns failures into issues, and tells your team what to fix before your users notice anything is wrong.
From millions of traces to what's actually broken
Tracing tells you what happened, but with millions of traces it rarely tells you what matters. Until now, finding a problem meant knowing where to look: filtering traces, opening them one by one, and hoping the failure you were chasing showed up in front of you.
With Confident AI Monitor, you start from the answer instead. Every trace is classified as it lands and graded with online evals, powered by the LLM of your choice or by Jev, which is fast and cheap enough to grade every trace in production rather than a sample. Failures that share a cause become issues, ranked by what's spiking, so the problem that started this morning sits above the one that has been quietly simmering for a month.
A root cause, the evidence, and a tested fix
An issue in Monitor is more than a label on a chart. Each one comes with the root cause behind it, the traces that show it happening, and a proposed fix that has already been tested against the failing cases.
That changes what your team spends its time on. Rather than hunting for problems and building each change by hand, your team reviews what Monitor found, decides whether the fix is right, and applies it in one click.
Works with the stack you already use
Monitor runs on the traces you already send to Confident AI, so there's nothing new to instrument. Whatever framework, model provider, or architecture your agent is built on, if it's traced, Monitor can read it. Any agent.
Built for the way your team already works
Keeping an agent healthy is rarely one person's job. The engineer on call needs to know what broke overnight; a product manager wants to know whether last week's change actually helped; a team lead needs to decide what gets fixed first.
Monitor brings that picture to the tools your team already uses. Ask Claude Code what broke overnight, and get your morning standup built for you: the new issues, what's spiking, and what to fix first, without opening a dashboard.
Traces tell you what happened. Monitor tells you what to fix.
Confident AI Monitor is available now. You can try it today in your Confident AI project, start sending traces with confident-trace, or book a demo to learn more.
Do you want to brainstorm how to evaluate your AI agents? Ask us anything in our discord. I might give you an "aha!" moment, who knows?
Evals for cross-functional teams. Not just engineers.
Give all AI use cases the same quality bar with all-in-one evals, observability, and red teaming, and enforce them at scale.