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.
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?
PandaProbe
@alexander_gray3 This is exactly what PandaProbe's session-level eval metrics are designed to surface. Rather than throwing a generic failure flag, they operate on distinct behavioral signals — tool correctness, confidence, coherence, and loop detection — each targeting a different failure mode. Tool call failures, hallucinations, and reasoning breakdowns all leave different signal patterns, and the metrics are built to catch and differentiate them across the full trajectory.
So instead of "something went wrong somewhere," you get a clear read on what type of failure occurred and where in the session it started. Silent degradation that never throws an error is exactly what this is built to catch.
Metric details here if you want to dig in: https://docs.pandaprobe.com/evaluation/agent-evaluation/metrics
Our research: TRACER, ICML 2026 → https://arxiv.org/abs/2602.11409
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?
PandaProbe
@amna9 Valid concern! For teams with strict data residency requirements, PandaProbe has an enterprise solution with on-premise deployment — your traces stay within your own infrastructure, VPC, or private cloud.
"Zero infrastructure to manage" is the Cloud promise — for enterprises where data residency is a hard requirement, we've got you covered. Happy to chat through the specifics 🙏
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?
PandaProbe
@ana_popescu2 PandaProbe ships with pre-built metrics out of the box — 9 trace-level metrics covering standard quality signals, plus two session-level metrics purpose-built for long-running agents: one measuring worst-case failure risk across the trajectory, the other measuring behavioral stability over time.
You don't start from scratch. You start with meaningful signal on day one, and can customize the parameters of each metric and eval run to better reflect what "good" looks like for your specific use case.
Full metric details here: https://docs.pandaprobe.com/evaluation/agent-evaluation/metrics
"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?
PandaProbe
@doganakbulut Thank you, and yes — the session as the unit of analysis is the core insight that makes everything else work for multi-step agents. Glad that resonated!
On the free tier: 100 trace ingestions, 100 trace eval runs, and 10 session eval runs per month — plus human annotation, all on a single seat. Enough to instrument a real agent, run meaningful evals, and get genuine session-level insights before spending anything.
Full breakdown here: https://www.pandaprobe.com/pricing 🙏
Triforce Todos
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?
PandaProbe
@abod_rehman Both, kind of! PandaProbe ships with pre-built metrics out of the box — 9 trace-level metrics plus two session-level metrics purpose-built for long-running agents covering failure risk and behavioral stability.
Custom metrics aren't supported yet, but you can customize the parameters of each metric and eval run to better reflect what "good" looks like for your specific use case.
It's on our roadmap — what kind of custom scoring would be most useful for you? Always helpful to know what people are actually building 🙏
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
PandaProbe
@boyuan_deng1 We go inside — not just log the trigger. PandaProbe's integrations intercept and capture MCP tool calls and their full context, so you get visibility into what actually happened inside the call, not just that it fired.
One honest caveat: if your MCP call involves multi-layer data access, there may be some context loss at the deeper layers. For the vast majority of MCP setups though, you get full trace coverage automatically.
Exactly the blind spot we set out to fix — would love to hear what stack you're running if you want to dig into specifics 🙏
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?
PandaProbe
@andrew_paul11 PandaProbe normalizes all provider-specific data into a universal trace schema — so whether you're running OpenAI, Claude, or Gemini, the trace format is identical across the board. No provider-specific quirks bleeding into your comparisons.
The pattern will feel familiar if you've worked with OpenTelemetry — same philosophy of provider-agnostic standardization, applied specifically to agent traces. Swap or mix providers without your tracing and eval setup breaking a sweat.