Catch the unknown unknowns before they become critical incidents. Build AI agents that automatically learn normal system behavior and instantly detect what static dashboards and hardcoded alerts miss.
An Anomaly Detection AI Agent is a continuous monitoring assistant that uses machine learning to understand the baseline behavior of your infrastructure, applications, and business metrics.
Instead of relying on rigid, manually configured thresholds that cause alert storms, the agent autonomously identifies unusual spikes, drops, and drifts, correlating them into actionable root-cause signals.
Automatic Baselines
Learns normal patterns and seasonality without manual threshold configuration.
Real-Time Detection
Instantly flags sudden deviations, silent degradation, and hidden systemic drifts.
Root Cause Signals
Correlates multiple anomalies into a single incident to reduce time-to-resolution.
Modern distributed systems fail in unpredictable ways. Relying on static rules creates blind spots and exhausts your engineering team.
Engineers receive hundreds of false-positive notifications daily from rigid thresholds, causing them to ignore actual critical warnings.
You can only write rules for problems you anticipate. When a novel failure mode occurs, static monitors remain entirely green.
Slow-moving degradations—like a 5% drop in checkout conversions over a week—rarely trigger hardcoded SLAs until revenue is lost.
When an incident happens, teams spend hours manually cross-referencing logs and metrics across 10 different dashboards.
A spike in traffic on Black Friday is normal. A spike at 3 AM on a Tuesday is an anomaly. Static rules treat both exactly the same.
As you deploy microservices, updating hardcoded thresholds for every new endpoint becomes an unsustainable operational burden.
Build specialized agents tailored to monitor different facets of your business—from infrastructure health to user behavior.
Monitors CPU, memory, latency, and error rates across all microservices, identifying silent degradations before they cause downtime.
Detects abnormal user access patterns, API abuse, sudden traffic spikes, and uncharacteristic drop-offs in feature adoption.
Flags inefficient scaling, runaway cloud jobs, and sudden cost drifts to prevent massive cloud billing shocks at the end of the month.
Learns standard login and access patterns to instantly flag compromised credentials, impossible travel, and unauthorized data exfiltration.
Monitors data pipelines and ML model inputs, alerting teams when statistical properties shift or unexpected null values appear in critical tables.
Tracks KPIs like checkout conversions, active users, and transaction volume. Alerts you immediately if business health drops inexplicably.
No data science degree needed. Connect your data sources, and let the AI build the statistical baselines automatically.
Start Building NowIntegrate with Datadog, AWS CloudWatch, New Relic, or stream custom JSON metrics directly via the RhinoAgents API.
Turnkey integrationsThe agent ingests historical data (typically 7 to 14 days) to understand daily and weekly seasonality, mapping what "normal" looks like.
Machine Learning modelAdjust the confidence interval bounds. Choose higher sensitivity for critical payment endpoints, and lower for background cron jobs.
Custom thresholdsTell the agent where to send anomalies. Route critical issues to PagerDuty and minor drifts to a dedicated Slack channel.
Smart routingActivate the agent. It will now continuously monitor the streams, correlating unusual spikes and sending you actionable root-cause insights.
24/7 MonitoringSee how AI anomaly detection transforms incident management.
SRE teams manually update hundreds of static threshold rules every time the application architecture or traffic changes.
AI continuously auto-baselines system behavior, adapting to new deployments and seasonal traffic automatically.
During an incident, 50 different microservices trigger independent alerts simultaneously, creating mass confusion and fatigue.
AI correlates the anomalies across systems and groups them into a single, cohesive incident report with the likely root cause.
Slow-moving memory leaks or gradual conversion drops go unnoticed for days because they never cross a hard "critical" line.
Agents catch subtle drifts in data distribution and raise early warnings before they compound into a full-scale outage.
Engineers spend valuable time hunting through logs and playing "find the metric" during stressful P1 incidents.
The agent automatically provides the top 3 deviating signals alongside the alert, instantly pointing to the "why".
Quantifiable improvements in system reliability and engineering productivity.
Less Alert Noise
Faster MTTR
Anomalies Detected
Continuous Monitoring
Salaries, shifts, benefits, and constant training.
Platform subscription. Infinite scale. No sleep needed.
Potential annual savings on monitoring operations
$241,600+
Freeing up your senior engineers for actual product development.
Everything you need to observe your system intelligently and resolve issues faster.
Sits on top of Datadog, Splunk, New Relic, and Prometheus to analyze the data you're already collecting.
Connects anomalies across different domains (e.g., matching a database latency spike to an upstream API error).
Tune the AI's strictness. Set bounds tighter for Tier-1 billing systems and looser for internal analytics dashboards.
Alerts don't just say "Anomaly". They include recent deployment tags, related logs, and likely contributing factors.
Intelligently routes the alert to the right team based on the microservice or signature of the anomaly detected.
Trigger automated remediation runbooks (like scaling up pods or restarting services) the moment an anomaly is confirmed.
Static rules were useless during extreme traffic spikes. The agent learned the expected high-load baseline and successfully isolated a failing payment gateway API.
Downtime
Faster RCA
False alerts
A misconfigured data pipeline started processing infinite loops. The anomaly agent detected the abnormal AWS spend trajectory in hours, saving thousands.
Saved in 24h
Cost tracking
Budget control
Attackers kept logins below standard rate-limiting thresholds. The AI caught the subtle behavioral drift in geographical login requests.
Attacks stopped
Detection time
Accounts breached
Paste this into RhinoAgents to instantly configure a baseline-learning Anomaly Detection Agent for your infrastructure.
You are an SRE Anomaly Detection Agent responsible for monitoring our production microservices. Your goal: Continuously analyze time-series metrics, establish statistical baselines, and detect anomalous behavior with high precision to avoid alert fatigue. Data Source & Context: - Connection: Datadog API integration. - Target Services: Checkout API, Authentication Service, Inventory DB. - Seasonality: High traffic from 9 AM to 5 PM EST on weekdays. Minimal traffic on weekends. Detection Rules: 1. Baseline Window: Learn from a rolling 14-day history. 2. Sensitivity (Tier 1 - Checkout): Alert if deviation exceeds 3 standard deviations for 2 consecutive minutes. 3. Sensitivity (Tier 2 - Inventory): Alert if deviation exceeds 4 standard deviations for 5 consecutive minutes. 4. Anomaly Types to monitor: Sudden latency spikes, step drops in throughput, and gradual error rate drifts. Output Formatting: When an anomaly is detected, trigger an incident report to the #sre-alerts Slack channel. The report MUST include: - The specific service and metric exhibiting the anomaly. - The expected baseline value vs. the current anomalous value. - A correlated list of any other metrics that drifted simultaneously (Root Cause Context). - A direct link to the relevant logs.
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The agent uses machine learning algorithms to map historical data patterns. It learns daily, weekly, and monthly seasonality—understanding that higher CPU usage on a Monday morning is normal, but the same usage on a Sunday night is an anomaly.
No, it enhances them. RhinoAgents connects to your existing APM and observability platforms via API. It acts as an intelligence layer on top of your metrics, finding the subtle anomalies that rigid dashboard alerts often miss.
Instead of firing 50 different alerts when a database slows down (triggering warnings across all dependent services), the AI correlates these simultaneous anomalies into a single, contextual incident report pointing to the database as the root cause.
You can feed the agent historical data via API for instant baselining. Typically, 7 to 14 days of historical metrics are enough for the agent to understand strong weekly seasonality and provide highly accurate detection.
Yes. While it's great for CPU and latency, it's equally powerful for monitoring checkouts per minute, login successes, or daily active users. If it's a time-series metric, the AI can detect anomalies in it.
Stop relying on static thresholds that cause alert fatigue. Build an AI agent that learns normal behavior and catches real incidents instantly.
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