Launching today
Progress AI Observability
Trace, evaluate, and improve AI agents in production
59 followers
Trace, evaluate, and improve AI agents in production
59 followers
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.
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