Agent tracing
Collect traces to inspect how an agent behaved across a run, which helps teams understand failures that do not show up in ordinary application logs.
Chirpz AI sviluppa PandaProbe, una piattaforma open source per tracciare, valutare e monitorare gli agenti AI in produzione, migliorando osservabilità e debugging dei workflow.
Chirpz AI is an applied AI lab focused on agent engineering. Its public-facing product is PandaProbe, an open-source agent engineering platform for traces, evals, and monitoring.
The site frames the product around a specific gap: AI agents need tooling for tracing, evaluation, and monitoring because they fail in ways that differ from traditional software and standalone LLMs. Chirpz AI says PandaProbe is built to help teams debug agents, improve them, and ship them with more confidence.
Collect traces to inspect how an agent behaved across a run, which helps teams understand failures that do not show up in ordinary application logs.
Evaluate agent behavior so teams can compare outputs and judge changes instead of relying on ad hoc manual review.
Monitor agents in production to watch for failures and quality regressions after deployment.
Treat observability as a first-class concern so debugging and improvement happen around the agent lifecycle rather than as an afterthought.
Use an open-source platform, which the company says it chose because transparency is foundational to the product.
Inspect agent traces to understand why a run failed, where behavior diverged, and what happened before an unexpected outcome.
Compare agent outputs with evals when changing prompts, models, or workflows so you can assess whether the change improved behavior.
Track agent behavior in production to spot regressions and monitor reliability over time.
Adopt an open-source agent engineering platform when transparency and inspectability matter to the team.
Chirpz AI presents PandaProbe as its product, an open-source agent engineering platform for traces, evals, and monitoring. The site describes it as a tool for debugging, evaluating, and improving AI agents in production.
The site says the team builds products and publishes research in agent engineering. Their stated focus is the gap between prototype and production for AI agents, especially observability, tracing, evaluation, and monitoring.
The source text does not describe a public signup flow, pricing tiers, or paid plans. The pricing page currently returns a 404, so pricing details are not available from the provided evidence.
PandaProbe is described as open source from day one, and the site links to GitHub and the pandaProbe.com domain. Beyond that, the provided sources do not list specific integrations.
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