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Kaval
Verify what your AI agent believes before it takes action
Your agent's worst mistakes won't look like mistakes. They'll be confident actions on cached facts, stored fields, and RAG chunks that quietly went stale. Kaval re-derives the truth the instant before your agent acts and returns a verdict to branch on: act, or don't. One MCP call, a typed pass/block with the proof.
Hey Product Hunt 👋
I'm Luis.
Here's the moment that made me build this. Our support agent told a customer their plan included a feature we'd removed a few weeks earlier. The detail it used had been right when it was saved, and by the time the agent answered, it wasn't. Nothing errored, nothing looked broken. It just confidently told someone something untrue.
The more I dug in, the more I saw the same thing everywhere. Agents rarely make things up out of thin air. They act on something that used to be true and quietly went out of date: a CRM field, a saved answer, a chunk from your knowledge base. By the time the agent uses it, the world has moved on and it has no idea.
Kaval is a quick check you run right before your agent acts. You hand it the fact it's about to rely on ("Acme is on the Enterprise plan") and it re-confirms it against live sources, then tells you whether it's still current, stale, contradicted, or whether it honestly can't tell. If the answer isn't safe to act on, your agent pauses and checks again instead of charging ahead.
A few things people seem to like:
- It gives a straight answer your agent can act on, plus the evidence behind it, so you're not handing a model ten links to re-read in the middle of a task.
- It actually understands that facts go out of date. An old job title or last quarter's price comes back as stale, even when it's still all over the web.
- When it isn't sure, it says so instead of guessing. I'd rather it tell me "I can't confirm this" than wave something through.
You can try it right now, no signup, at https://usekaval.com. Type any belief into the box near the top and watch it check it live. If you want to wire it into your own agent, there's an MCP server and Node and Python SDKs behind a free key.
I'd genuinely love your help finding where it's wrong. Give it a fact it should catch and tell me if it misses, or one it flags that's actually fine. That's the most useful thing you can throw at me today, and I'll be in the comments all day.
LAUNCH40 gets you free credits if you want to run it on your own agent. 🙏
Luis
the re-derive step is the right instinct but what's the added latency per action in practice, and does it scale down for agents that fire dozens of tool calls a minute? feels like there's a tradeoff between catching stale facts and slowing the whole loop down, curious how you tuned that
spent a weekend stress-testing kaval against our agent's worst habit, hallucinating from stale data, and the typed pass/block with the proof feels like exactly what we needed to catch silent failures before they hit users.
How does Kaval handle the latency cost of re-deriving truth on every single agent action, especially for chains with hundreds of steps?
About Kaval on Product Hunt
“Verify what your AI agent believes before it takes action”
Kaval was submitted on Product Hunt and earned 18 upvotes and 6 comments, placing #35 on the daily leaderboard. Your agent's worst mistakes won't look like mistakes. They'll be confident actions on cached facts, stored fields, and RAG chunks that quietly went stale. Kaval re-derives the truth the instant before your agent acts and returns a verdict to branch on: act, or don't. One MCP call, a typed pass/block with the proof.
Kaval was featured in Developer Tools (515.4k followers), Artificial Intelligence (473.1k followers) and Bots (110.7k followers) on Product Hunt. Together, these topics include over 184.1k products, making this a competitive space to launch in.
Who hunted Kaval?
Kaval was hunted by Luis Fernando Monteiro Cerqueira. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Luis Fernando Monteiro Cerqueira