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.
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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?
@carter_son You're right — pre-deployment is where most of the pain actually lives.
Native CI/CD integration is currently in development — 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 🙏
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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?
@daniel_juan2 This is actually where PandaProbe's approach has a natural advantage. Our eval metrics don't compare outputs against a ground truth — 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" — 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 — behavioral consistency is measurable even when correctness isn't.
Congrats on the launch! As agents become more complex, observability is quickly turning from a nice-to-have into a requirement.
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looks solid . one question, purely from the security pov. are you gdpr compliant and what about usage data that you store
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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?
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An idea for future development: instead of only evaluating responses, as most tools do, you could also add automatic benchmarking against other popular models. That would be extremely useful. We'd definitely use something like that ourselves.
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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_son You're right — pre-deployment is where most of the pain actually lives.
Native CI/CD integration is currently in development — 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 🙏
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_juan2 This is actually where PandaProbe's approach has a natural advantage. Our eval metrics don't compare outputs against a ground truth — 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" — 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 — behavioral consistency is measurable even when correctness isn't.
Stripo.email
Congrats on the launch! As agents become more complex, observability is quickly turning from a nice-to-have into a requirement.
looks solid . one question, purely from the security pov. are you gdpr compliant and what about usage data that you store
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?
An idea for future development: instead of only evaluating responses, as most tools do, you could also add automatic benchmarking against other popular models. That would be extremely useful. We'd definitely use something like that ourselves.