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).
We spent the last year on a research problem: can you train a production-grade eval or guardrail from just a task description, no labeled data, no annotation pipeline?
Turns out you can. We call it vibe-training.
Most teams today rely on LLM as a judge. It never fully converges, breaks on edge cases, and at 100ms per call it collapses economically at scale. So teams sample instead of evaluating everything. Failures happen between the samples, invisibly.
Plurai lets you describe what your agent should and should not do. The platform generates training data, validates it through a multi-agent debate process, and deploys a custom small language model in minutes.
Results against GPT-5 LLM-as-judge: over 43% fewer failures, 8x lower cost, sub 100ms. Good enough to run on every interaction, not just a sample.
The research behind it is public.
Try it free at https://app.plurai.ai, I'd love to hear what eval problem you're working on.
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?
Generating training data from a task description instead of labeled datasets removes a big bottleneck, but it also shifts risk into how well the task is written. How sensitive is the system to vague or underspecified prompts?
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.
vibe-training as a concept is interesting — how does it handle drift over time once the agent's prompt or tool surface changes? curious if you re-run the eval generation or if it's a one-time thing.
The multi-agent debate validation is the part I want to understand better. How do you keep the debate from converging on the same model's biases? Different model families per agent, or the same base with different role prompts? Asking because validation-by-consensus often inherits failure modes from the underlying judge, and avoiding that is the actual hard problem.
wow looks amazing @Plurai congrats with the launch
Love it. The product looks great and super proffesional!
I'm just wondering can it help with any type of models or only textual models for now?
If I'm working with VLMs, or with LLMs in a pipeline but processing audio, still images or video it could help with any model as long as it's dealing with language and semantics ?
Vibe training is such a good framing, finally something that matches how teams actually think about agent behavior. cheers team 🙌 BTW, what happens when two guardrails conflict with each other at runtime?
@tammy_wolfson2 Many congrats on PH launch. Quick Question, does Plurai auto-detect model drift and retrain, or is that a manual trigger?
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.
Congrats on the launch, does it work with all LLMs that provide fine-tunning capabilities?
About Plurai on Product Hunt
“Vibe-train evals and guardrails tailored to your use case”
Plurai launched on Product Hunt on April 29th, 2026 and earned 672 upvotes and 218 comments, earning #1 Product of the Day. 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).
Plurai was featured in API (98.1k followers), Developer Tools (511.7k followers) and Artificial Intelligence (467.2k followers) on Product Hunt. Together, these topics include over 166.2k products, making this a competitive space to launch in.
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fmerian
Hey Product Hunt, Ilan from Plurai here.
We spent the last year on a research problem: can you train a production-grade eval or guardrail from just a task description, no labeled data, no annotation pipeline?
Turns out you can. We call it vibe-training.
Most teams today rely on LLM as a judge. It never fully converges, breaks on edge cases, and at 100ms per call it collapses economically at scale. So teams sample instead of evaluating everything. Failures happen between the samples, invisibly.
Plurai lets you describe what your agent should and should not do. The platform generates training data, validates it through a multi-agent debate process, and deploys a custom small language model in minutes.
Results against GPT-5 LLM-as-judge: over 43% fewer failures, 8x lower cost, sub 100ms.
Good enough to run on every interaction, not just a sample.
The research behind it is public.
Try it free at https://app.plurai.ai, I'd love to hear what eval problem you're working on.