Root cause analysis usually starts the same way: an engineer opens five dashboards, greps through logs, and tries to remember whether anything changed recently. PulseServe's RCA Agent runs that process automatically the moment an incident is enriched.
It pulls from a two-hour window of MELT data — metrics, errors, logs, and traces — alongside alert patterns, change correlations, blast radius, and the CI dependency graph. Recent changes are weighted by how recent they are (a change 15 minutes ago scores highest) and how directly they touch the affected CI, so the agent is not just pattern-matching against noise.
Two things make the output trustworthy rather than speculative. First, if the incident matches an entry in the Known Error Database, that pre-validated root cause is used and confidence is automatically set to 0.9 or higher. Second, the agent checks for "cascade siblings" — other incidents firing from the same blast-radius group — because incidents that share a blast radius are a strong signal they share a root cause. Past RCAs that a human has already approved are fed back in as few-shot examples, so the model's reasoning improves with every incident your team confirms.
The result is structured, not a paragraph of prose: a root cause hypothesis, a confidence score, an evidence chain a reviewer can actually check, immediate actions, prevention steps, and suggested knowledge-base article titles so the fix gets documented as a byproduct of resolving it.
None of this replaces the engineer — it replaces the twenty minutes of dashboard-hopping that used to happen before the engineer could start actually thinking about the fix.