No coding. No complex ML model configurations. Describe your business metrics, acceptable variance boundaries, and Slack/Teams channels — RhinoAgents builds the monitoring flows, connects to your stack, and watches your systems 24/7.
An AI agent is software that monitors systems, metrics, and databases, dynamically calculating baselines and alerting on deviations. Unlike traditional alerts with static limits, it understands weekly traffic patterns, correlates errors across metrics, and diagnoses root causes autonomously.
Learns seasonal traffic patterns, understanding that lower weekend checkouts are normal while weekday drops indicate issues.
Correlates changes in API response codes with SQL transaction rates to map checkout drops to specific gateway issues.
Traces errors, reviews server logs, and cross-checks Git commits to isolate the exact bug causing the anomaly.
Filters out minor temporary spikes, alerting only on structural failures with high-fidelity diagnosis reports.
// Anatomy of an AI Agent
Secure. Reliable. Go from query prompts to active anomaly detection in under an hour.
Write a prompt detailing your KPIs, e.g. "Monitor Stripe revenue and checkout page latency."
Specify boundaries like "Flag deviations above 3x rolling weekly averages."
Link Snowflake, BigQuery, Prometheus, Datadog, or API end points.
Point to your server logs, GitHub releases, or status page URLs for root cause tracing.
Activate the agent to monitor and send alerts via Slack, Teams, or PagerDuty.
// Example prompts that build real monitoring agents on RhinoAgents
"Build an AI agent that monitors Stripe transaction volumes. If the transaction success rate drops below 95% within a 10-minute window, alert the engineering Slack channel and trace database logs for payment API issues."
"Create an AI agent that runs a SELECT count query on Snowflake prod databases every 5 mins. If query times spike above 2s, check DB connection pools and notify on-call engineers."
Monitor active data scans, dynamic baseline margins, and diagnostics real-time.
Static bounds and siloed metrics trigger too many false alarms, masking real issues. Here is how operations leak, and how AI plugs every gap.
Rigid limits trigger false alarms during normal peak events, causing engineers to ignore metrics when true failures hit.
Silent silent failures or logic loops pass standard health pings, staying undetected until users complain.
Database errors, gateway logs, and payment APIs are checked separately, losing correlation links when metrics dip.
When checkouts break, on-call teams spend hours sorting logs and checking configurations to find the bug.
PagerDuty notifications fire on minor system fluctuations, draining developer morale and slowing productivity.
Bugs introduced in new code updates go unnoticed until databases slow, with no clear link to the code deploy.
Each agent is purpose-built for database and system metrics monitoring. Describe what you need — RhinoAgents handles the rest.
Monitors critical SQL queries, API transaction volumes, and latency spikes.
Traces anomaly roots across database logs, API statuses, and code releases.
Formats anomaly warnings with diagnostic logs and commits, updating Slack channels.
Adjusts seasonal metrics margins based on user behavior and weekly traffic cycles.
Watches database query execution patterns to find and alert on slow queries.
Monitors external webhook responses and checks REST API endpoint integrity.
Monitors active connection pool volumes, locks, and system performance metrics.
Triggers on-call escalation rules in PagerDuty or Opsgenie on severe anomalies.
See how automated anomaly monitoring compares directly against typical static dashboard tools.
Yes. You can paste any SQL query directly into the prompt box, and the AI agent will run it continuously on Snowflake, BigQuery, PostgreSQL, or Redshift, establishing baselines and alerts on the result value.
You can update the AI context dynamically via chat or API. For example, telling the agent 'We are running a flash sale at 12:00' instructs it to automatically expand baseline bounds to prevent false alarms during high-traffic events.
We support Snowflake, BigQuery, Datadog, Prometheus, Elasticsearch, PostgreSQL, MySQL, Stripe API, Segment, and GitHub deployment webhooks, as well as direct JSON API monitoring.
Yes. RhinoAgents uses enterprise-grade credential management systems, read-only permissions scopes, and TLS 1.3 encryption. We strictly analyze aggregated metrics and metadata, never storing personal patient or user tables.