PandaProbe Cloud gives your team full-stack tracing, evals, and monitoring for agents with zero infrastructure to manage. Ship better agents without the ops overhead.
A while back we launched the open-source version here — the response was incredible. Today we're back with what many of you asked for: PandaProbe Cloud — full-stack tracing, evals, and monitoring for agents, with zero infrastructure to manage.
Here's a pattern every agent builder knows: you ship, it looks fine in testing, then quietly misbehaves in production — nobody knows why. Once agents start chaining LLMs, tools, APIs, MCPs, and sub-agents, debugging becomes archaeology. Logs tell you something happened — not why, not whether quality regressed, not how the session held together. And solving that shouldn't mean building your own agent engineering stack.
That's PandaProbe Cloud: ship better agents without the ops overhead.
What you get 🔎 Tracing — full agent executions captured as sessions, traces, and spans. 📊 Evaluation — score traces and sessions using SOTA agent-specific metrics. ⏱️ Monitoring — schedule recurring evals to track your agent's health in production. ☁️ Fully managed — we handle the infra. You just connect, ship, and improve.
Who it's for 🧑💻 AI engineers debugging agent behavior across LLMs, tools, and workflows. 🏗️ Platform teams monitoring quality and reliability without owning more infra. 🔬 Builders experimenting with agents who want to iterate faster. 🚀 Startups who want production-grade observability from day one.
The open-source version saved me during a nasty multi-agent debug a few months back, so this is exciting. Does the eval scoring work for custom agent frameworks or is it tied to specific SDKs?
I like the focus on making agent debugging and monitoring easier. As agent workflows become more complex, having better visibility into what's happening behind the scenes is incredibly valuable.
The fully managed approach is a big plus too.
"debugging becomes archaeology" lol yeah. once one action turns into 5 tool calls and a couple sub-agents the logs just tell you the order stuff happened, not why it went wrong
how does the eval side deal with non-determinism though? same input gives slightly different traces every run, so how do you score regressions without drowning in false positives
For MCP-heavy agents, I’m curious how session grouping works in Cloud. Do MCP tool calls get attached to one session timeline automatically, or is that only reliable if I pass the same session_id through each layer?
About PandaProbe Cloud on Product Hunt
“agent engineering, fully managed.”
PandaProbe Cloud launched on Product Hunt on June 15th, 2026 and earned 85 upvotes and 6 comments, placing #7 on the daily leaderboard. PandaProbe Cloud gives your team full-stack tracing, evals, and monitoring for agents with zero infrastructure to manage. Ship better agents without the ops overhead.
PandaProbe Cloud was featured in Open Source (68.5k followers), Developer Tools (514k followers), Artificial Intelligence (471k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 207.4k products, making this a competitive space to launch in.
Who hunted PandaProbe Cloud?
PandaProbe Cloud was hunted by Sina Tayebati. 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 PandaProbe Cloud stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Sina Tayebati
👋 Hey Product Hunt!
I'm Sina, founder of PandaProbe.
A while back we launched the open-source version here — the response was incredible. Today we're back with what many of you asked for: PandaProbe Cloud — full-stack tracing, evals, and monitoring for agents, with zero infrastructure to manage.
Here's a pattern every agent builder knows: you ship, it looks fine in testing, then quietly misbehaves in production — nobody knows why. Once agents start chaining LLMs, tools, APIs, MCPs, and sub-agents, debugging becomes archaeology. Logs tell you something happened — not why, not whether quality regressed, not how the session held together. And solving that shouldn't mean building your own agent engineering stack.
That's PandaProbe Cloud: ship better agents without the ops overhead.
What you get
🔎 Tracing — full agent executions captured as sessions, traces, and spans.
📊 Evaluation — score traces and sessions using SOTA agent-specific metrics.
⏱️ Monitoring — schedule recurring evals to track your agent's health in production.
☁️ Fully managed — we handle the infra. You just connect, ship, and improve.
Who it's for
🧑💻 AI engineers debugging agent behavior across LLMs, tools, and workflows.
🏗️ Platform teams monitoring quality and reliability without owning more infra.
🔬 Builders experimenting with agents who want to iterate faster.
🚀 Startups who want production-grade observability from day one.
Quickstart:
☁️ Cloud signup: https://app.pandaprobe.com/
🤖 Run: npx skills add chirpz-ai/pandaprobe-skills --skill '*' --yes
💥 Then ask your coding agent to "set up PandaProbe".
Free to start — generous usage credits. Up and running in minutes.
Quick links
📖 Docs: https://docs.pandaprobe.com
⭐ Open source: https://github.com/chirpz-ai/pandaprobe
I'll be here all day — drop your questions and feedback below.
Thanks for checking it out 🙏
— Sina