The engineer on call often knows the remediation before the dashboard finishes rendering: the sidecar is wedged, roll the deployment. What eats the hour is assembling the evidence, first for themselves and then for whoever asks about it on Friday. DataAgent proposes to reverse that order, applying a fix the customer pre-approved through its own guardrails against live state, topology and configuration drift, and running the deeper root-cause work offline once service is back [7][8].
The cost argument leans on someone else's number. Grafana Labs' 2025 observability survey put that spending at an average 17% of total compute infrastructure spending, with 10% the most common answer [6]. The gap is the interesting part. The mean sits 7 points above the mode, 1.7 times it [17], which is the shape of a distribution where a minority of estates pay enormously and drag the average up. Take DataAgent's claim that customers running it alongside existing tools can cut up to 90% of the observability bill [16] and multiply through: 15.3% of compute spend recovered at the average, 9% at the mode [18]. The ceiling is the vendor's own, unaudited, and "up to" is carrying weight in that sentence.
Shalom calls autonomy an architectural shift rather than a feature layered onto observability [12]. What actually gets installed is narrower and more useful than that: an inventory. Onboarding opens with a discovery phase that tells the customer which classes of failure the software can already fix by itself, with a timeline for the rest and a route to a human for anything it has not been proven correct on [13]. Approved fixes go out through the change management process the team already runs, or one command line instruction [14]. So the month-one deliverable is a written list of faults the organisation will let software close unsupervised. Most teams do not have that list, and producing it has value even if the purchase never happens.
Yaari's version of the misalignment is blunt: when a vendor's revenue is your data ingest, it cannot cut your bill without cutting its own [11]. That test applies to the challenger too. The agent is open source and free to run standalone, with the money in a SaaS tier for fleet management and orchestration [10], but the source does not say what that tier costs or what unit it meters [19]. Priced per cluster or per node, it still grows with the estate, just on a different axis than gigabytes ingested.
Two questions sort the work. Can you enumerate the failure class in advance, and is the fix reversible without data loss? Enumerable and reversible (restart the wedged pod, roll back the bad config) is the quadrant to hand over, and it is probably most of the pages. Enumerable but irreversible, meaning anything touching stateful data or a migration, stays with a human however good the discovery report looks. Not enumerable but reversible is the candidate list you revisit after a quarter of logged outcomes. Neither is where you keep paying for retention and for people who can read it.
The savings depend on that last quadrant shrinking, and DataAgent is careful to say the platform sits above whatever monitoring stack is already there rather than replacing it, sending data outward only when a fault needs deeper inspection [9]. Someone still has to decide which telemetry stops being shipped, and that decision is where the 90% would have to come from.
