No coding. No alert fatigue. Describe your routing and runbook rules — RhinoAgents builds, connects to your monitoring stack, and resolves incidents 24/7.
An Incident Management AI Agent is software that audits system alerts, executes runbook diagnostics, and coordinates remediation steps autonomously. Unlike basic webhook trigger scripts, it reasons through server logs, restarts containers, and updates status updates without code.
Separates low-priority warnings from critical outages, preventing developers from experiencing alert fatigue.
Executes server checks, database cleanup scripts, container restarts, or traffic re-routing policies immediately.
Gathers relevant logs, CPU stats, and trace paths during outages so the on-call team has instant access to root causes.
Initiates shifts in PagerDuty, alerts Slack channels with timelines, and drafts customer-facing status page updates.
// Anatomy of an Incident Agent
No developers. No custom alert rules. Go from runbook wikis to an active AI agent in under an hour.
Write a plain-English prompt like "Audit Datadog alerts and execute restart if CPU spikes"
Alert Triager, Runbook Assistant, or Post-Mortem Writer template configurations.
Link PagerDuty, Datadog, AWS, GCP, Slack, JIRA, and Kubernetes in seconds.
Upload system runbooks, debug checklists, and approved command sequences.
Activate the agent. It begins monitoring metrics and resolving devops outages.
// Example prompts that build real incident agents on RhinoAgents
"Create an AI agent that monitors Datadog warning alerts. Group related database alerts together and only page PagerDuty if the database cluster is unresponsive for 5 minutes."
"Build an AI agent that monitors our disk space alert. If disk usage exceeds 92%, run clean log scripts. If it remains high, request backup pod allocations and alert Slack."
Monitor webhook alert triage, runbook execution steps, and system recovery cycles real-time.
Slow runbook executions and alert noise exhaust developer teams. Here is where yield leaks, and how AI plugs every gap.
Critical engineers get dozens of duplicate database or warning alerts at night, causing burnout and missed issues.
Resolving standard alerts (like disk cleanup or node restarts) requires waking up devs to execute wiki runbooks manually.
On-call developers are paged but have to manually collect logs, CPU metrics, and trace histories, delaying MTTR.
Post-outage timelines are forgotten or poorly documented because writing summaries takes hours of dev time.
Customers experience downtime before developers update status pages, generating duplicate support tickets.
Known database lockups require the same manual query kills repeatedly because root fixes take weeks to schedule.
Each agent is purpose-built for devops workflows. Describe what you need — RhinoAgents handles the rest.
Groups noisy warnings and filters duplicate alerts from Datadog.
Runs shell commands and pod restart playbooks automatically.
Searches and parses log traces to isolate error root causes.
Routes P1 escalations to the correct on-call engineer.
Drafts and publishes public incident updates during outages.
Assembles event logs and drafts JIRA Root Cause Analysis timelines.
Performs Kubernetes deployment resets based on failed health probes.
Isolates compromised AWS/GCP instances and logs audit traces.
See how automated incident response compares directly against typical manual engineering setups.
Yes. By connecting AWS Systems Manager, SSH, or Kubernetes API keys, the agent can execute shell restarts or database queries safely.
You configure dry-run boundaries or enforce human-in-the-loop approvals via Slack before any mutating shell command runs.
It connects directly to Datadog, PagerDuty, New Relic, Kubernetes, AWS, GCP, Slack, JIRA, and Statuspage.
Build your first DevOps AI agent today. No coding required. Start with a free trial.
No credit card required · Setup in under 60 minutes · Cancel anytime