Debug and monitor AI agent failures in minutes. Trace every run, catch hallucinations and ungrounded answers that traditional monitoring misses, and see exactly what went wrong. Reduce token waste, improve agent quality, and ship faster with support forNET, Python, and JavaScript.
I’m Lyubo, the Product Manager behind Progress AI Observability.
We know a lot of teams are building AI agents now. The first demo comes together quickly but then you try to run it in production and things get more complicated.
Traditional monitoring can tell you that an app is running, but it usually can't explain why an agent chose a particular tool, ignored useful context, entered an expensive loop or produced an answer that looked convincing but was wrong.
We built Progress AI Observability to give engineering teams that missing visibility.
We want to move teams from “something went wrong” to understanding why it happened, what needs to change and whether the next version is actually better.
You can start tracing in minutes with support for .NET, Python, and JavaScript/TypeScript. There’s a free plan, and no credit card is required.
We'd love to hear your feedback on:
What’s hardest to debug once an agent reaches production?
Which signals are most useful to you: traces, evaluations, latency, token usage, or cost?
What would you need to see before using this with a production agent?
Thanks for checking it out 🙏 - Lyubo and the Progress AI Observability Team
I assume if I want to monitor how Claude Code works, then I have to use Fiddler? (I love Fiddler)
@yochev Congratulations. And happy product launch.
Really like how this makes “successful but wrong” agent runs visible. Tracing the full flow and tying it to evals is a powerful combination.
About Progress AI Observability on Product Hunt
“Trace, evaluate, and improve AI agents in production”
Progress AI Observability launched on Product Hunt on August 7th, 2026 and earned 160 upvotes and 15 comments, placing #6 on the daily leaderboard. Debug and monitor AI agent failures in minutes. Trace every run, catch hallucinations and ungrounded answers that traditional monitoring misses, and see exactly what went wrong. Reduce token waste, improve agent quality, and ship faster with support forNET, Python, and JavaScript.
Progress AI Observability was featured in SaaS (43.5k followers), Software Engineering (42.8k followers) and Artificial Intelligence (475.5k followers) on Product Hunt. Together, these topics include over 171.5k products, making this a competitive space to launch in.
Who hunted Progress AI Observability?
Progress AI Observability was hunted by Lyubomir Atanasov. 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.
Want to see how Progress AI Observability stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Lyubomir Atanasov
Hey Product Hunt 👋
I’m Lyubo, the Product Manager behind Progress AI Observability.
We know a lot of teams are building AI agents now. The first demo comes together quickly but then you try to run it in production and things get more complicated.
Traditional monitoring can tell you that an app is running, but it usually can't explain why an agent chose a particular tool, ignored useful context, entered an expensive loop or produced an answer that looked convincing but was wrong.
We built Progress AI Observability to give engineering teams that missing visibility.
We want to move teams from “something went wrong” to understanding why it happened, what needs to change and whether the next version is actually better.
You can start tracing in minutes with support for .NET, Python, and JavaScript/TypeScript. There’s a free plan, and no credit card is required.
We'd love to hear your feedback on:
What’s hardest to debug once an agent reaches production?
Which signals are most useful to you: traces, evaluations, latency, token usage, or cost?
What would you need to see before using this with a production agent?
Thanks for checking it out 🙏
- Lyubo and the Progress AI Observability Team