Martin Yochev

On August 7, we launched Progress AI Observability on ProductHunt
We were thrilled to share our product with the community there and so grateful for all the support!
Here are a few lessons from the experience:
1 Tell one story really well.
AI observability can get complex fast, so we focused on one familiar problem: an AI agent gives a confident answer, everything looks healthy, but the answer is wrong. Then we showed how tracing and evaluations help uncover why.
Lesson for anyone launching: you have seconds of people's attention. One specific story is often more powerful than ten features.
2 It (really, really) is about the community
Publishing the page is only the start. We spent time engaging on Product Hunt, talking with people interested in agents and AI, and asking people in our network to share the launch with others who d genuinely find it relevant. That's what actually helped spread the news, not just creating a great launch page.
3 Decide what success actually means.
Upvotes are visible, but they re only one signal. For us, success was mainly about people trying the product, giving us feedback, asking questions + looking at the broader impact of the launch.
So the lesson was: measure the halo, but don t stop at traffic (or just refferral traffic). Measure what people do once they arrive and what you learn from them.

And finally, if you're building AI agents, our product is for you. Try Progress AI Observability for free (yes, it's an actual free tier, not just a trial) and see what s actually happening inside every run https://prgress.co/4xTazIL

AI agents can fail in ways traditional monitoring was never designed to catch.

A run may look healthy while the agent:

• chooses the wrong tool

• ignores relevant context

• produces an unsupported answer

• gets stuck in a costly loop

• burns more tokens than expected

We made Progress AI Observability to help you see what actually happened.