Voker

What it takes: evals & observability

The proof is in the pie. Yesterday @voker_ai launched on Product Hunt, and we finished in the top ten. As promised... for every 10 upvotes I got pied in the face.

The AI agent companies that win won't just build the best agents. They'll be the best at knowing when their agents are failing. @daltonc backed us because he sees it too. "The companies that are best at instrumenting and improving their agents will win." - Dalton Caldwell, Co-Founder @Standard_cap, Partner-Emeritus @Ycombinator

This is likely the most impactful update you can make to your agent. Most companies that hire me have no idea how their customers are using their agents. More importantly, they don't know whether agents are delivering on their promises. They have zero visibility and analytics about their agents. This is always my first recommendation and the one that makes everyone's life easier. Voker is a very simple solution. They are partnering with me on this post. What I like about them is their simplicity: • Simple to install (one library and one line of code) • Simple to operate (a simple dashboard for the entire team) • Built for conversational agent products In return, you get automatic analytics with insights about how your app is running and where it is failing. Building this from scratch would take weeks. Here, you get it all without moving a finger. You can check them out here: voker.ai Their free tier offers unlimited seats and lets you log 2,000 events every month, so you have plenty to experiment with.

How to enable full observability and automatic analytics for your LLM-based application. It takes one library + one line of code, and you get a ton of information for free. This is a no-brainer.

We're live on Product Hunt today.

Voker is live. Free up to 2000 events/mo voker.ai and check us out on Product Hunt: producthunt.com/products/voker…

Agent teams are flying blind in production. We built the analytics layer to fix that.

Shipping AI agents but still flying blind on why users keep rephrasing the same request? Voker by @Voker_ai auto-classifies intents, corrections, and resolutions from every conversation so your whole team can see what's breaking. More in the next tweet. #DevTools

VOKER FEST 2026 🎈

RSVP luma.com/vo7ee392

& that's a wrap. Road to GA, week 10.

We don't even have a shared vocabulary for how multi-agent systems fail yet. That's what we've been building toward. Intents, Corrections, Resolutions: a standard way to measure what users wanted, where agents fell short, and whether they recovered. Voker launches May 19th

Your agent is making decisions you can't see. Voker shows you the full picture: what the user intended, where the agent corrected course, and how it resolved. All mapped to the exact moment in the conversation. Automated Insights > digging through logs

Enterprise AI in 2026: billions spent on Copilot seats, inference costs through the roof, and the team in charge still has no idea which agents are actually working. Measurement is the unglamorous work nobody wants to do, until something breaks in production. We're launching May 19th to fix this.

We still have some tricks up our sleeves...

@theneurondaily theneurondaily.com/p/google-is-ki…

Very cool to be in today's @theneurondaily! Thanks to Grant Harvey for the shout out in "Treats to Try".

90% of AI teams only find out their agent is failing when a customer complains. Then someone spends half a day reading through individual traces trying to figure out what went wrong. We got tired of watching this happen. Live on Hacker News today.