Astrea turns a live incident into a structured, queryable model of what changed, then reasons over it to point engineers straight at the likely cause.
Production incidents are not solved by dumping more context into an LLM. Coding agents reason over snapshots of code, not the live system. At scale, logs, traces, deploys, configuration, and runtime state become too noisy and fragmented to interpret reliably.
Astrea reconstructs the incident as it unfolds. It correlates evidence across the production system, identifies the root cause, proposes and validates mitigation, and produces the postmortem—while showing the evidence behind every conclusion and action.
Low-AI-adoption periods are indexed to 100. High-adoption values show the resulting relative level.
Source: Faros AI Engineering Report 2026; 22,000 developers across 4,000+ teams.
Pull every relevant log line, trace, deploy diff, and metric series into one timeline the moment the incident fires.
Rank the signals. Surface the symptoms that matter, the services on fire, and the recent changes that look causally connected.
Form hypotheses against the evidence and walk engineers to a fix, with the reasoning shown at every step.
Track the fix in flight. Confirm error rates fall, downstream effects clear, and no new failure modes appear.
Close the loop with a structured post-incident record: causal chain, signals used, decisions made, and gaps left to harden.
We are onboarding engineering teams in small batches. Join the waitlist or set up a call to see how reasoning infrastructure fits your stack.