Launched this week
PandaProbe Cloud
Agent Engineering, Fully Managed.
730 followers
Agent Engineering, Fully Managed.
730 followers
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.
Free Options
Launch Team / Built With
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?
PandaProbe
@antonio_manuel1ย Great question and an important one to get right. The short answer is that PandaProbe is designed around targeted monitoring โ not firehose logging. The session and trace model means you're capturing structured, meaningful signal rather than raw log volume, which naturally keeps data footprint lean.
On retention and pricing specifics for your scale, best to chat directly so we can tailor the right setup for your usage patterns rather than give a one-size-fits-all answer.
Feel free to email meโ happy to walk through it ๐
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?
PandaProbe
@carlos_leonardo1ย PandaProbe handles this by flushing spans after each step execution โ so no matter how long an agent runs, whether it's minutes, hours, or days, every step is captured incrementally as it happens. You're not waiting for the agent to complete before data shows up.
For pause and resume scenarios, as long as the session ID is carried through the instrumentation wrapper, trace continuity is maintained automatically when the agent picks back up. Same goes for spawned sub-agents โ they get attached to the parent session timeline as long as the session context propagates through.
Long-running agents are exactly the use case PandaProbe is built for โ the session model was designed with this in mind from the start ๐
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?
PandaProbe
@carter_sonย You're right โ pre-deployment is where most of the pain actually lives.
Native CI/CD integration is currently in development โ it's high on our roadmap for exactly this reason. In the meantime, happy to chat through what's possible with the current SDK and CLI while we get there.
Stay tuned, it's coming soon ๐
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?
PandaProbe
@daniel_juan2ย This is actually where PandaProbe's approach has a natural advantage. Our eval metrics don't compare outputs against a ground truth โ they measure behavioral signals: confidence, coherence, tool correctness, loop detection.
For open-ended outputs where there's no single correct answer, that distinction matters a lot. You're not asking "did the agent produce the right answer" โ you're asking "did the agent reason reliably, use tools correctly, and maintain coherence across the trajectory." Those questions have meaningful answers even when the output space is completely open-ended.
It's the same reason session-level reliability is a more robust signal than output matching โ behavioral consistency is measurable even when correctness isn't.
Just curios how is it different from langfuse/smith?
PandaProbe
@naor_sabagย Great question โ LangSmith and Langfuse are built around the trace as the unit of analysis. They're great at showing what happened inside individual LLM calls. PandaProbe is built around the session โ the full agent lifecycle โ as the unit of analysis.
That distinction matters for long-running, multi-step agents. A trace tells you one step went wrong. A session tells you whether the agent was reliable across the entire trajectory โ and our eval metrics, grounded in peer-reviewed research, are purpose-built to catch that. (TRACER, ICML 2026 โ https://arxiv.org/abs/2602.11409)
But honestly, tracing and evals are just the foundation. We're building towards something bigger โ a complete agent engineering platform that gives teams everything they need to develop, evaluate, and continuously improve agents in production. This launch is the first chapter of that vision. ๐
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