features
See the whole stack, from reasoning to the GPU
Trace one agent from reasoning to GPU
ee the tool calls, models, and retrieval steps of a workflow from request to response, then connect that behavior to the infrastructure it ran on. Root cause shows up on one timeline, not in four disconnected tools.
See the infrastructure underneath
Track GPU and memory use, power, and time-to-first-token across models, vector databases, and the rest of the AI stack. When a token surge meets a saturated GPU, the slow response stops being a mystery.
Read a failed run step by step
Walk a single timeline from an ECC spike to a thermal or power anomaly, to Xid 79 and a dead run, with the agent trace and the GPU telemetry side by side. The answer to "why did it fail?" lives in one view.
See the cascade before the run fails
Catastrophic failures rarely arrive without warning. The early signals are visible, so correlated telemetry can warn you while there is still time to recover the run before it dies at hour 47.
Know what is healthy and what is at risk
Watch availability and health across the AI stack so degradation surfaces early. Catch the rising ECC count or the saturating cache before it becomes a dead run at hour 47.
Built on Splunk, correlated with Cisco
Pure-play AI tools see the prompt but never the chip. General-purpose monitoring sees the GPU but never the agent's reasoning or the network fabric. Splunk and Cisco span the full stack, so a bad answer and the silicon that caused it land in one place.
