Deploy RhinoAgents' AI Observability Agent to unify your logs, metrics, and traces into a single intelligent layer. Detect anomalies instantly, correlate cross-service signals, and let AI do the heavy lifting of root cause analysis — before your customers feel the impact.
Logs · Metrics · Traces · Anomalies
RhinoAgents' AI Observability Agent turns raw telemetry into actionable intelligence. It ingests logs, metrics, and traces across your entire stack, applies AI reasoning to detect anomalies, correlate incidents, and surface root causes — automatically and continuously, 24/7.
Ingests logs, metrics, and distributed traces from any source — Datadog, Prometheus, OpenTelemetry, Grafana, Splunk, and more — into a unified AI reasoning layer for holistic analysis.
Detects statistical anomalies in real time across latency, throughput, error rates, and custom metrics — without relying on static thresholds that cause false alarms.
Automatically correlates spans across microservices to identify exactly which service, endpoint, or dependency caused a slowdown or failure — with full trace waterfall visualization.
Uses LLM reasoning over correlated signals to explain incidents in plain English — identifying probable root cause, affected services, blast radius, and recommended fix — in seconds, not hours.
Groups related alerts into single correlated incidents, suppresses noise from flapping signals, and delivers only high-confidence, actionable notifications to your on-call engineers.
Learns from historical telemetry patterns to detect early warning signals — gradual memory growth, rising p99 latency, increasing retry rates — and alerts before they escalate into full outages.
Purpose-built for DevOps and SRE teams who need more than dashboards — they need intelligence that explains what's happening, why it's happening, and what to do about it.
The agent continuously parses structured and unstructured logs from your applications, infrastructure, and cloud services. It uses NLP to extract meaningful patterns, flag error signatures, and group related log events — eliminating the need to manually grep through millions of log lines during incidents.
Instead of static alert thresholds, the AI Observability Agent uses dynamic baselines built from your historical metrics. It detects deviations that matter — even subtle ones — across CPU, memory, network I/O, request rates, error budgets, and custom business metrics from Prometheus, Datadog, or Grafana.
Integrates with OpenTelemetry, Jaeger, Zipkin, and Tempo to collect and analyze distributed traces across microservices. The agent identifies the slowest spans, pinpoints latency hotspots, and maps dependencies so your team understands the exact failure path within seconds.
When an incident is detected, the agent generates a plain-English explanation: what broke, which services are affected, what the probable cause is, and what steps engineers should take next. No more context-switching between dashboards — get the full picture in a single AI-generated summary.
Automatically correlates signals across logs, metrics, and traces to connect the dots between a noisy alert and its upstream cause. The agent maps symptom → contributing factor → root cause, giving your SRE team a clear chain of evidence instead of isolated data points.
Monitors your error budgets and SLO burn rates in real time. The agent alerts when burn rate accelerates beyond safe thresholds, gives you burn rate projections, and integrates with your incident management tools to open tickets automatically when SLAs are at risk.
Dramatically reduces Mean Time to Detect (MTTD) by catching anomalies the moment they emerge, and Mean Time to Resolve (MTTR) by providing engineers with instant root cause context. Teams using RhinoAgents report up to 70% reduction in MTTR on production incidents.
After every incident, the agent auto-generates a structured post-mortem report: timeline, root cause, impact scope, contributing factors, and recommended action items. Export directly to Confluence, Notion, or your ITSM platform to close the loop without manual documentation.
Built with OpenTelemetry as a first-class citizen. Instrument once and send telemetry to any backend. The agent supports OTLP ingest natively, making it easy to adopt without ripping out existing observability infrastructure. Extend via RhinoAgents' flexible API framework for any custom data source.
RhinoAgents' AI Observability Agent connects natively to your existing observability stack. No rip-and-replace — plug in via APIs, OTLP, or native SDKs and start getting AI-powered insights within hours.
Our OTLP-compatible API supports any telemetry source your stack emits.
We don't just add another dashboard to your stack. RhinoAgents layers AI reasoning on top of your existing observability tools to deliver intelligence, not just data.
You don't need to rip out Datadog or Grafana. RhinoAgents sits on top of your existing observability tools as an AI intelligence layer — enriching the data you already collect with reasoning, correlation, and explanation capabilities.
Traditional war rooms take hours of manual log diving. The AI Observability Agent correlates signals, identifies the blast radius, and delivers a root cause hypothesis within seconds of incident detection — so your engineers fix things, not investigate them.
Legacy monitoring tools require you to manually set thresholds for every metric. Our agent builds dynamic baselines from your actual traffic patterns, adapting automatically to business cycles, deployments, and seasonal load — eliminating the toil of threshold management.
Whether you run 10 microservices or 10,000, the AI Observability Agent scales horizontally with your architecture. Add new services and they're automatically instrumented, baselined, and monitored — without any manual configuration overhead.
See how engineering teams are using AI-powered observability to slash MTTR, eliminate alert fatigue, and ship with confidence.
Eliminating Noisy Alerts
Challenge: Their SRE team was receiving over 4,000 alerts per day from Datadog and Prometheus. On-call engineers suffered from chronic alert fatigue, with most alerts being duplicates, false positives, or noise from flapping services. Genuine incidents were getting lost in the noise.
Solution: RhinoAgents' AI Observability Agent was layered on top of their existing Datadog setup. The agent applied correlation intelligence to group related alerts into single incidents, built dynamic baselines to eliminate false threshold breaches, and used AI reasoning to surface only the alerts that required human attention.
"We went from 4,000 alerts a day drowning our on-call rotation to a manageable stream of high-confidence incidents with full AI-generated context. Our engineers sleep better now."
— Alex Torres, Principal SRE
Microservices Latency Debugging
Challenge: A fintech running 200+ microservices on Kubernetes struggled to debug latency spikes on their payment APIs. Engineers spent hours correlating traces across Jaeger, logs in Elasticsearch, and metrics in Grafana — all in separate tools with no unified view.
Solution: RhinoAgents' AI Observability Agent connected their OpenTelemetry pipeline, Jaeger traces, and Grafana metrics into a single reasoning layer. The agent automatically identified which microservice span was the latency bottleneck and cross-referenced it with recent deployment changes to pinpoint the cause.
"What used to take three engineers two hours to investigate now takes the AI agent under 90 seconds. The trace correlation is genuinely magical."
— Priya Mehta, Engineering Lead
Error Budget & SLO Tracking
Challenge: Their platform had aggressive SLOs for peak shopping periods but no real-time visibility into error budget burn rates. SLO breaches were discovered after the fact, leading to customer impact and reactive war rooms during high-traffic events like Black Friday.
Solution: The AI Observability Agent was configured to monitor SLO burn rates across all critical endpoints in real time. When burn rate accelerated beyond safe thresholds, the agent sent predictive warnings with projected time-to-breach, enabling the SRE team to intervene proactively before customers were impacted.
"We used to find out about SLO breaches from customer complaints. Now the AI agent warns us 30-45 minutes before we'd breach our budget. That's a fundamentally different way to operate."
— James Okafor, VP Engineering
Answers to the technical questions DevOps and SRE teams ask most about our AI Observability Agent.
Stop reacting to incidents after the fact. Let the AI Observability Agent watch your telemetry, explain your incidents, and predict your outages — so your team ships with confidence.