Every model call, agent step, and eval in one place — with the token bill attached and the upstream data that shaped the answer one hop away.
Most AI tools stop at the trace. ByteShift connects the model call to the pipeline that fed it and the invoice it ran up — so a quality drop arrives with its cause, not just a red line.
gen_ai spans from agents and SDKs — prompt, completion, tokens, and wall-clock — reassembled into sessions you can replay turn by turn. Whether it arrived as a client span or an agent tool event, it’s captured.
AI adoption across the org from the telemetry you already send — which tools, which teams, which models. The picture no eval tool gives you: org-wide, cost included, never guessed from a price list.
Gate on evals and watch quality over time. When a model regresses, it opens an incident anchored to the agent — with the eval run and the upstream change already attached as evidence.
A stale corpus degrades your support agent; a retry storm triples the token bill. In ByteShift that’s one incident — the schema change on orders_raw at the top, the agent and the spend joined by lineage, root cause first.
Because AI, data, infrastructure, and cost all emit into the same entity graph, the fix points upstream instead of at the model — and your coding agent gets the brief with the evidence attached.
Point your OTLP exporter at ByteShift and your first agent call shows up as an entity. No schema to design, no pre-registration.