Kalibr fixes failures as your agents run, using real-time user behavior and structured evals. No redeploy. No human in the loop. No downtime.
Kalibr runs as a layer underneath your agents. It watches every step using two signals: structured evals on the output, and how your real users respond. When something goes wrong, Kalibr adapts the agent on the next run, automatically.
Bad outputs, broken format, timeouts, the wrong model for the task. Kalibr sees it inline, before the run completes.
Structured evals on the output, plus what your users actually did with it. Accept, reject, edit, abandon, re-prompt. That's the data Kalibr uses to choose the fix.
Every run teaches Kalibr what works for your agents and your users specifically. Future runs start smarter, without you redeploying anything.
Most teams default to flagship models because flagship is the safe choice. The cheaper models would work fine most of the time, but if they fail, the user sees it. Kalibr's eval layer is what makes downgrading safe.
Kalibr evaluates the output structurally and against user behavior on every step. If the cheap model produced something good, you keep it.
If the eval fails, Kalibr escalates to a stronger model on the same step. The user never sees the bad output. You only pay premium where it actually mattered.
Over time, Kalibr knows which steps in your pipeline genuinely require frontier models and which don't. Routing gets cheaper without quality dropping.
When a user reprompts, edits, or rewrites your agent's output, most agents have no idea which step in their pipeline caused the problem. They regenerate the whole thing and hope. Kalibr attributes the user's signal to the specific step, then fixes that step on the next run.
Or edits the output, abandons it, asks for a different format, or tells the agent it got something wrong. Most agents see this as a vague signal and regenerate everything.
It correlates what the user said back to which step's output triggered it. Was it the retrieval call that pulled stale context? The generation step that used the wrong tone? Kalibr knows.
Kalibr changes the model, tool, or prompt at exactly the broken step. The user gets the right output faster. The reprompt loop closes.
If your agents are in production, you already know what happens. They worked in demos. Now keeping them running is somebody's full-time job. Kalibr is what closes that loop, so the people who built your agents can build the next thing.
Failures heal automatically instead of becoming tickets. The portion of agent runs that need a human to recover them goes down. The volume your team can support goes up.
Frontier models where they earn it. Cheaper models everywhere they perform just as well. Kalibr learns the difference from your actual production data, not a benchmark.
Runs that would have failed silently or stopped halfway now finish. Your customers get the output they came for. Your product feels reliable instead of flaky.
Kalibr adapts your agents in production without touching your code. The improvements ship as they're learned. Your release cycle stays free for actual product work.
Kalibr correlates outcomes across user behavior, model, tool, and prompt. When a similar agent solves a similar problem, your agents inherit the priors. Anonymized. Never shared between customers.
Kalibr sees what's working across every agent on the platform: which models, prompts, and recovery strategies complete work for which kinds of tasks. Those priors feed your agents from the first run.
Your data stays yours. The patterns Kalibr learns are stripped of identifying context before they ever leave your tenant. The network is intelligence, not exposure.
Usage-based pricing for teams running agents in production. No per-seat. No annual minimums until you want them.
Your agents recover, adapt, and improve in production. You go back to building your product.