Progress AI Observability
Product Hunt Lyubomir Atanasov
A new tool called Progress launched on Product Hunt promising to trace, evaluate, and improve AI agents once they're live. It's aimed at the messy reality of running AI in production, not just testing it in a demo.
Based on reporting by Product Hunt, Lyubomir Atanasov — read the original for the full story.
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Every AI team eventually hits the same wall: the agent that worked beautifully in a Jupyter notebook starts behaving strangely once real users get their hands on it. Progress, a new entrant on Product Hunt, is pitching itself as the fix for that gap — an observability layer built specifically for AI agents rather than traditional software.
The pitch is simple on its face: trace what an agent actually does step by step, evaluate whether those steps produced a good outcome, and then use that feedback to improve the system. That's a different job than standard application monitoring, which mostly watches for crashes and latency spikes. Agents fail in subtler ways — they hallucinate a tool call, loop on a bad plan, or quietly drift from the instructions they were given. Catching that requires visibility into reasoning chains and decisions, not just server logs.
This space has been heating up fast. As more companies push agentic AI into customer support, coding assistants, and internal automation, the failure modes have gotten expensive and public. A support bot that invents a refund policy or a coding agent that merges broken code isn't a rounding error — it's a support ticket, or worse, a headline. Tools like Progress are betting that teams need dedicated infrastructure to catch these issues before they reach users, the same way APM tools became standard for web services a decade ago.
What's notable is the positioning around production, not development. Plenty of tools help debug agents while you're building them. Fewer are built for the ongoing job of watching agents that are already shipped and serving real traffic, where the stakes and the data volume are both higher. If Progress can make that loop — trace, evaluate, improve — genuinely continuous rather than a one-time audit, it's solving a problem that's only going to get bigger as more of these systems go live.
My take — AI-written commentary, not fact-checked reporting
Observability for agents is going to be one of the more boring-sounding but genuinely necessary categories in AI infrastructure, right up there with vector databases a couple years ago. Companies keep shipping agents faster than they can explain what those agents actually did, and eventually someone in finance or legal is going to ask for a trace nobody kept. The tools that win here won't be the flashiest — they'll be the ones that make debugging a black box feel almost normal.
Read more about this at: Product Hunt
