Watch every signal. Explain every incident. Predict outages. Describe what your operations and SRE team need — RhinoAgents builds your AI agent in minutes.
An AI agent is software that understands telemetry context, correlates logs, metrics, and traces, and resolves issues autonomously. Unlike traditional static threshold alerts, an observability agent connects directly to your platform APIs to run root cause diagnostics and post-mortems.
Parses structured and unstructured logs using NLP, grouping related events and flagging anomalies instantly.
Integrates natively with Datadog, Prometheus, Grafana, Jaeger, and OpenTelemetry to query live spans and metrics.
Groups noisy logs and spikes, detects contributing factors, and drafts incident explanations in Slack.
Monitors burn rates, predicts timeline-to-breach, and alerts on-call rotations before customers notice.
// Anatomy of an Observability AI Agent
No complex integrations code. Go from text prompt to active telemetry monitoring in under an hour.
Write a prompt like "Monitor Datadog metrics, suppress noisy alerts, and explain root causes in Slack"
Log Parser, Metric Monitor, Trace Correlator, Post-Mortem Writer — or a multi-functional SRE assistant
Link Datadog, Grafana, OpenTelemetry, Slack, Jira, and PagerDuty in one click
Drop in your runbooks, SLO definitions, past post-mortems, and service diagrams
Deploy the agent to listen to your OTLP streams. Watch it suppress alerts and generate root causes
// Example prompts that build real observability agents
"Create an AI agent that listens to checkout API latency spikes, correlates traces with postgres logs, finds connection leaks, and recommends rolback decisions in under 90 seconds."
"Build an AI agent that aggregates Slack incident logs, telematics metrics, and trace timelines to auto-generate structured markdown post-mortems and publish them to Confluence."
Stop digging through logs manually during production outages. Build an AI agent to handle it.
Each agent is purpose-built for SRE workflows. Define yours in minutes.
Continuously parses error logs, identifies code exception patterns, and alerts developers.
Builds dynamic baselines for throughput and memory without manual threshold configurations.
Links spans across microservices to isolate latency hotspots in under 90 seconds.
Generates plain-English summaries of alerts, blast radius, and suggested remediations.
Tracks error budgets and burn rates, predicting time-to-breach during latency surges.
Auto-generates incident timelines, contributing factors, and exports reports to Confluence.
Suppresses redundant alert signals and duplicates, routing only high-priority events.
Learns from telemetry trends to alert on slow memory exhaustion hours before outages.
Stop wasting critical on-call hours on noise, manual log parsing, and stale retro reports. Seal every gap.
Flapping warnings create thousands of alerts, drowning out high-priority incidents.
• Group alarms dynamically
• Deduplicate alert warnings
• Route only valid notifications
Engineers context-switch across logs, metric panels, and traces to manually correlate causes.
• Correlate trace loops automatically
• Pinpoint exception logs in seconds
• Highlight pool connection leaks
Outages are caught too late, depleting your error budget before alerts fire.
• Monitor burn rates in real time
• Predict exact time-to-breach
• Trigger proactive scaling rules
Hard-coded alerts fail to adapt to traffic peaks or code commits, causing constant false alarms.
• Build rolling dynamic baselines
• Adjust dynamically to seasonal load
• Eradicate threshold tuning toil
Reconstructing incident timelines takes days, leading to undocumented lessons.
• Auto-generate incident timelines
• Draft clean retro summaries
• Export to Confluence automatically
Rotating engineers start from scratch because they lack clear cross-platform summaries.
• Deliver instant Slack summaries
• Map blast radius coordinates
• Provide immediate remediation steps
Real conversation flows your built AI agent handles automatically — every time, at any hour.
AI detects, correlates, and surfaces root cause — instantly, without human investigation.
What this agent handles
No more war rooms. AI delivers the full incident picture in plain English.
What this agent handles
Know about SLO breaches before customers do — and before your error budget is gone.
What this agent handles
Structured incident documentation created automatically — exported to Confluence, Notion, or Jira.
What this agent handles
Outcomes reported by SRE and DevOps operations that deployed observability agents.
Groups related alerts automatically, preventing on-call fatigue.
From hours of war rooms to under 90s context-rich root causes.
Real-time burn tracking warns teams before budget breaches occur.
Proactive telemetry scans identify low memory or connection leaks early.
Dynamic rolling baselines eliminate manual alert rule drift.
AI post-mortem tracking prevents repeated outages.
Whether you run 10 microservices or 10,000 — monitor your telemetry in minutes.
Cut MTTR and automate post-mortems
Link deployments with latency logs
Track platform error budgets dynamically
Detect anomalous access logs 24/7
Automated observability on a low budget
No rip-and-replace. Connect your tools and launch.
"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."
An AI Observability Agent continuously watches your telemetry data — logs, metrics, and traces — to detect anomalies, explain incidents, correlate signals across services, and suggest root causes. RhinoAgents' agent goes beyond traditional monitoring by using AI to reason across your entire observability stack and surface actionable insights in real time.
Traditional monitoring tools generate thousands of noisy alerts. Our agent uses correlation intelligence to group related signals, suppress redundant alerts, and surface only the alerts that matter — along with context, probable cause, and suggested remediation steps. This dramatically reduces alert volume your SRE team has to manage.
RhinoAgents integrates with Datadog, Grafana, New Relic, Prometheus, Jaeger, OpenTelemetry, Splunk, Dynatrace, and Elastic. It can ingest logs, metrics, and traces from any of these sources via API connectors or OTLP natively.
No. RhinoAgents is designed as an intelligence layer on top of your existing observability tools, not a replacement. Your Datadog dashboards, Grafana panels, and Prometheus alerts all continue to work as before. The AI agent connects via APIs to enrich your existing telemetry with AI-powered reasoning and root cause analysis.
Build your first AI observability agent in minutes — with a simple prompt. No code. Connect your tools and go live.
No credit card required · Setup in under 60 minutes · Cancel anytime