PandaProbe

PandaProbe is a tracing and evaluation platform purpose-built for production AI agents. Unlike static test suites that only sample outputs, it captures every tool call, LLM hop, and conditional branch so evaluation scores reflect the full trajectory an agent actually takes. This level of observability is critical for teams running multi-step agents where small errors compound across reasoning chains. The platform applies research-grounded evaluation methods, including long-horizon uncertainty metrics and LLM-as-judge scoring, to surface precisely where an agent drifts from expected behavior. Rather than relying on a single pass/fail signal, PandaProbe quantifies confidence across extended task horizons, making it easier to diagnose fragile reasoning steps and prioritize fixes. For teams running agents in production, PandaProbe schedules evaluation runs against live traffic and alerts stakeholders when key metrics regress between model or prompt versions. This continuous monitoring loop turns agent evaluation from a one-off QA step into an ongoing reliability practice, helping teams catch silent degradations before users do. The tool is designed to fit agent-native workflows. A Skill file, command-line interface, and Python SDK allow engineering teams to wire PandaProbe directly into popular LLM providers and coding agents without heavy integration overhead. Setup is intentionally lightweight so teams can begin tracing and scoring agents quickly. PandaProbe is best suited for AI engineers, ML researchers, and product teams who need rigorous, reproducible evaluation of autonomous agents at scale. By combining deep trajectory tracing with statistically meaningful evals, it helps bridge the gap between prototype demos and dependable production systems.