Launched this week
Vibe training for AI agent reliability. Describe what your agent should and should not do — Plurai generates training data, validates it, and deploys a custom model in minutes. It feels like vibe coding, but for evaluation and guardrails. No labeled data. No annotation pipeline. No prompt engineering. Under the hood, small language models deliver sub 100ms latency, 8x lower cost than GPT as judge, and over 43% fewer failures. Always on, not sampled. Built on published research (BARRED).
Asa.team
The part that stands out to me is the economics argument. LLM-as-judge at 100ms per call means you're forced to sample, and failures happen in the gaps between samples. That's a real problem we've run into.
Curious about the drift question though: once the agent's prompt or tool surface changes, how much of the vibe-training do you have to redo? Is there a way to do incremental updates or does a significant prompt change basically mean starting fresh?
Also interested in whether the small model you deploy is hosted by Plurai or exportable. For anything touching sensitive data the deployment model matters a lot.
This is a really clever approach to the eval problem. As someone who's spent way too many hours trying to wrangle GPT-4 into being a consistent judge for my agent outputs, the "vibe training" framing actually makes a lot of sense — describing behavior in natural language rather than crafting elaborate rubrics.
The sub-100ms latency is what catches my attention most. For agents that need real-time guardrails (not just batch evaluation), that's the difference between usable and not usable in production.
Curious how this handles edge cases that emerge after deployment — is there a feedback loop to refine the model when it misses something in the wild?
Plurai
We talked to hundreds of AI teams before building this.
The same thing kept coming up: evals are on the roadmap, always. They just never get done. Too slow, too expensive, someone needs to label data, someone needs to set up a pipeline, and suddenly it's a Q3 project that rolls into Q4.
That's the problem we actually solves.
Describe what your agent should and shouldn't do, and you have a custom model running in minutes. Not a prototype. In prod.
Launching today and genuinely excited about it.
Go try it free: app.plurai.ai. Come back and tell me what eval problem you're working on.
Plurai
@omri_sela2 🚀
Plurai
@omri_sela2 can you believe it's finally out??
Plurai
@reut_v_plurai our baby 👶
minimalist phone: creating folders
So does it prevent AI agents from purchasing overpriced courses, right? :D
Plurai
@busmark_w_nika 😅 it can!
Plurai
@busmark_w_nika Yes and more:)
Plurai
@busmark_w_nika did you get a chance to try it out yourself?
minimalist phone: creating folders
@tammy_wolfson2 I only tried one prompt, but at the moment I do not haev any data to train on.
Tested it during the weekend and it’s amazing!!!
Plurai
@eduardo_ordax great to hear!
Plurai
@eduardo_ordax amazing!
awesome! make sure to leave a review here: https://www.producthunt.com/products/plurai/reviews/new
Plurai
@eduardo_ordax what did you like most?
Plurai
@eduardo_ordax glad you love it!
Ok, you've got me. My product uses agents (for coding) and quality is the #1 concern, so if I can get evals and scores, I'm hooked. Heading over to your site. Take my upvote.
lfg, Robert! give it a spin - go to plurai.ai and add your review here: https://www.producthunt.com/products/plurai/reviews/new
Plurai
@robert_douglass exactly what we were aiming for! what did you think?
Plurai
@robert_douglass amazing! Happy to hear
Plurai
@robert_douglass thank you!
Toone
It's looking real nice. Could an MCP be applicable here?
Plurai
@matheus_paranhos1 Coming very soon 👀
Plurai
@matheus_paranhos1 Great question! coming really soon :)
@ilankad23 spoiler alert 🙈
Plurai
@ilankad23 @fmerian Haha 😆
Plurai
@fmerian @tammy_wolfson2 indeed, this is just the beginning