Observability means querying logs, metrics, and traces across every service, at full cardinality, in real time — so an agent (or an engineer) can actually find the rare, novel incident, not just the ones that survived downsampling.
When something breaks, every minute to root cause shows up as lost revenue, blown SLAs, and eroded trust. Many observability stacks control cost through sampling, aggregation, or shorter full‑resolution retention. That can remove the rare clue a novel incident depends on. An investigating agent does not know what is missing; it reasons over the picture that survived and can commit confidently to the wrong cause.
Full‑resolution data remains queryable across the retention window you configure, with hot and object‑storage tiers controlling cost. The rare incident remains available to a human or an agent instead of being averaged away before anyone looks for it.
One engine handles the broad scan and the single fast lookup into one trace — each query returns sub‑second, at full resolution, so root cause surfaces in seconds instead of after the outage has already run long.
Each investigation can write confirmed correlations, rejected hypotheses, and its operator history into a tenant‑scoped reasoning layer. Later agents build on that evidence while policy changes remain subject to your approval process.
Full reasoning is preserved and auditable, so when an agent commits to a root cause, a human can check exactly what data and reasoning got it there — instead of taking a confident, black‑box verdict on faith in the middle of an incident.
One agent confirms the deploy correlation. Another refutes it — the regression is regional, not global. A third proposes an alternative: a retry storm from one tenant. The swarm weighs all three and tells you which one the data actually supports.