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 managed eval scheduler stands out here. For agent teams, continuously checking production behavior feels more useful than only debugging after something breaks. Do teams usually start with live traffic or replayed traces?
looks solid . one question, purely from the security pov. are you gdpr compliant and what about usage data that you store
Congrats on the launch! As agents become more complex, observability is quickly turning from a nice-to-have into a requirement.
Evals in isolation can be misleading if the ground truth itself is ambiguous how does PandaProbe handle eval scoring for open ended agent outputs where there's no single correct answer to validate against?
Monitoring agents in production is one thing but catching regressions during development is where most teams bleed time how tightly does PandaProbe integrate into CI/CD pipelines for pre deployment eval runs?
How does PandaProbe handle tracing for long running agents that might operate over hours or days and does it maintain trace continuity when an agent is paused, resumed or spawns sub agents mid execution?
Agent monitoring generates enormous volumes of trace data very quickly what's your data retention and cost model and how do you help teams avoid paying for signal they'll never actually look at?
For teams running agents across multiple LLM providers simultaneously how does PandaProbe normalize tracing data so comparisons between GPT-4, Claude and Gemini outputs are actually meaningful and consistent?
Evals are only as useful as the criteria they are measuring against does PandaProbe come with pre built eval frameworks for common agent behaviors or do teams need to define their own success metrics from scratch?
Zero infrastructure to manage is a bold promise for enterprise teams with strict data residency requirements how does PandaProbe handle organizations that can't send agent traces to an external platform for compliance reasons?
Most agent failures happen silently in production how does PandaProbe differentiate between a hallucination a tool call failure and a reasoning breakdown when surfacing what actually went wrong in a trace?
Agent observability gets messy once the same user task spans tools + retries, so the session/traces/spans framing makes sense. One thing I'd check before wiring this into prod: can evals be versioned against both prompt/model changes and tool schema changes? Otherwise a regression dashboard can get a little misleading after an MCP/API update.
Congrats on the Cloud launch. The session-as-the-unit framing feels right for agents, because individual traces are not enough once tools, MCP calls, and subagents start branching.
One edge case I’d love to understand: if an agent fans out into parallel subagents and some tool calls continue after the parent task has already moved on, how does PandaProbe decide what still belongs to the same session timeline? Is it based on explicit session IDs, automatic propagation, or both?
Does this actually trace inside MCP tool calls or just log that they were triggered?
That's always been the blind spot for me with most observability tools
Been waiting for something like this honestly. Quick question, the agent evals, are those pre-built metrics or can you define what "good" looks like for your own use case?
"Debugging becomes archaeology" is painfully accurate once subagents and tool calls start chaining. Making the session (not the trace) the unit of analysis is a smart angle for multi-step agents. Congrats on the Cloud launch! How generous are the free tier credits to start experimenting?
Hey @sina_tayebati , qq if we start on cloud but need to migrate back to self-hosted open source later because of data residency laws, is the data schema 100% compatible?
About PandaProbe Cloud on Product Hunt
“agent engineering, fully managed.”
PandaProbe Cloud launched on Product Hunt on June 15th, 2026 and earned 283 upvotes and 48 comments, placing #5 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 (68.6k followers), Developer Tools (515.4k followers), Artificial Intelligence (473.1k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 218k 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