Turn vibes into confidence
Find out how your AI agent fails, then improve it on quality, latency, and cost
Vibes
#agent-updates
sarah · 9:14 AM
We shipped the new model yesterday. How's it looking?
marco · 9:31 AM
Feels better? I tried a few prompts and chatted with it, not sure tbh 🤷
sarah · 9:36 AM
Okay, let's keep a close eye on customer support tickets for the next week in case we need to roll back
marco · 9:41 AM
👍 I'll skim some transcripts tomorrow and flag anything that looks off. No idea how many is enough though
Confidence
#agent-updates
marco · 9:02 AM
We shipped the new model yesterday, everything looking good. So far bookings have increased 4.2% with 90% confidence.
marco · 9:04 AM
p50 latency is down 38%, tail latency slightly elevated — will look into it. Overall cost is down 24%.
+4.2% vs control · 90% credible interval
“You changed the agent. Did it actually get better?”For most teams, the honest answer is still: we don't know.
You don't know how your agent fails
Users hit failures you never thought to test for, and nobody has counted which ones happen most or which touch revenue.
You can't tell if a change actually helped
You swap in the latest model, but how does it perform on your use case? What happened to quality, latency, and cost?
Your data sits there doing nothing
Every request you've served is training data for a model of your own. Most teams keep renting.
Your failures, grouped, named, and ranked
Told a guest the deposit was refundable. The rate is non-refundable.
3 min ago · high
Quoted a 48-hour cancellation window. The rate has 7 days.
11 min ago · high
Answered a parking question with nothing in the listing to back it.
26 min ago · medium
Lost the new dates after the guest changed the booking.
41 min ago · medium
Every failure mode, named
Failure modes discovered from your own traffic, ordered by how often they hit, how severe they are, and the business metric they hurt.
Surprises get caught
The same groupings across production, offline, and live, so a fix that creates a new problem is caught early.
No evals to maintain
One AI judge scores every session, and those scores are correlated with the quality metrics you already report.
Find the best AI configuration for your use case
Every candidate is graded by an AI judge and measured against the metrics your business already reports. Statistical confidence across quality, latency, and cost.
Live experiment · Hotel bookings completed
Each bar shows the 50% and 90% credible interval around that variant's booking rate, against the control line at 68.4%.
Failure clusters
vs controlfailures across these clusters
412 → 315 sessions
319 → 231 sessions
146 → 152 sessions
0 → 24 sessions
Experiments you can run in an afternoon
Candidate prompts, models, and configs run against requests you have already served. Quick to set up, and nothing reaches a user.
Measured on your own metric
Bookings, resolutions, merged PRs, whatever you already report, with an AI judge scoring every session alongside it.
A failure-mode diff, every time
Which clusters improved, which got worse, and if any new clusters emerged.
Your data is your moat
Your production traffic and your experts' assessments become a specialized language model built for your use case — better, faster, and cheaper than the frontier model it replaces. No ML team required.
Find your Pareto frontier across closed and open models — or break it entirely with a custom model tailored to your use case
Beats the frontier where it counts
A general model has to be good at everything. Yours only has to be good at one thing: your specific task.
The dataset builds itself
Your production traffic is filtered and curated into training data automatically.
Your experts' judgment compounds
Every grade and explanation your team provides is signal the model learns from.
Integration
One prompt away
Paste one prompt into your coding agent. It installs the three.dev skills, wires your LLM calls through the proxy, and keeps the AI SDK you already use.
Agent onboarding prompt
Ship on evidence, not vibes
Your next agent change deserves better than “LGTM, ship it.”