CraveStudio's intelligent agents detect infrastructure issues, diagnose root cause, and apply the right fix — learning from every resolution to get faster and more accurate over time.
Pod crashes at 3 AM. OOM kills during peak traffic. Deployment failures on Friday evening. Your team is stretched thin, alerts pile up, and MTTR keeps climbing.
Most incidents follow patterns your team has seen before. The same root causes, the same diagnostic steps, the same remediation actions — repeated manually every time. That's where CraveStudio's agents step in.
Intelligent agents that handle incidents the way your best SRE would — 24/7 and without fatigue.
Connects to your monitoring stack (Prometheus, Datadog, PagerDuty, CloudWatch) and correlates alerts across systems. Deduplicates noise and surfaces genuine incidents.
Pulls relevant logs, metrics, and recent deployment history. Cross-references with the learning system to identify probable root cause — often in seconds, not minutes.
Applies the appropriate fix: restart pods, scale resources, rollback deployments, or rotate credentials. Escalates unfamiliar patterns to your team with full context.
Each agent builds knowledge of your specific infrastructure — service dependencies, normal baselines, team ownership, and historical resolution patterns. It gets smarter every week.
Start in supervised mode where every action requires approval. As the agent proves accuracy, gradually increase autonomy. You control the pace — always.
Every detection, diagnosis step, and remediation action is logged with timestamps and reasoning. Complete transparency for post-incident review and compliance.
Pre-built connections to Kubernetes, Prometheus, Grafana, PagerDuty, Slack, Jira, and many more tools. No custom development required.
"We went from 45-minute average MTTR to under 8 minutes. The platform resolved 200 incidents perfectly in supervised mode — after 3 weeks we moved to autonomous. It now handles 80% of infrastructure incidents without any human involvement."
It escalates to your team with full context — the alert details, diagnostic steps already taken, and probable hypotheses. Your team resolves it, and the platform learns from that resolution for next time.
Most teams see accurate autonomous resolution within 2-4 weeks. The first week is spent learning your environment in supervised mode. By week 2-3, it's accurately proposing fixes. By week 4, most teams enable autonomous mode for known incident patterns.
The graduated autonomy model prevents this. In supervised mode, all actions require human approval. In autonomous mode, the platform has configurable guardrails — blast radius limits, rollback triggers, and automatic escalation if metrics don't improve after a fix.
It augments your team by handling repetitive, known incidents automatically — typically 60-80% of alert volume. Your engineers still handle novel situations, architecture decisions, and complex multi-system failures. They just get paged far less often.
CraveStudio supports webhook-based integrations for any tool that can send HTTP notifications. Custom integrations can also be built during onboarding for enterprise plans.
Predict resource needs and prevent outages with continuous analysis of utilization patterns.
Learn more →Automate password resets, software access, and common requests. Reduce ticket volume by 70%.
Learn more →Monitor deployments, detect failures, and auto-rollback when health checks degrade.
Learn more →15-minute live demo using a real Kubernetes environment. Bring your most common alert — we'll show you how CraveStudio would handle it.
Schedule a Demo →60-day pilot guarantee • Dedicated onboarding • First workflow live in 2 weeks