Portfolio Project · AIOps · Kubernetes
Autonomous Incident
Commander
AI-powered incident detection, root-cause analysis, and safe remediation for Kubernetes. Reduces MTTR by 50%+. Cuts alert noise by 40%. Resolves 60%+ of common incidents without shell access.
◈ PLATFORM METRICS — LIVE
MTTR IMPROVEMENT
simulated incidents
▲ vs baselineALERT NOISE CUT
dedup + grouping
▲ via dedup + groupingAUTO-RESOLVED
no manual shell access
AVG RESOLUTION
triage to close
◈ CONTROL LOOP ARCHITECTURE
Telemetry Ingestion
Alertmanager webhook → normalize alerts, pull Loki logs, fetch rollout history → canonical IncidentContext object
AI Reasoning
LangChain agent retrieves similar incidents + runbooks via FAISS, prompts LLM with structured evidence, returns typed hypothesis
Policy Gate
Confidence threshold check, namespace allowlist, action allowlist. High-risk actions route to human approval queue
Safe Remediation
Kubernetes Python client executes restart/scale. ArgoCD REST API triggers rollback. Post-action health polling confirms recovery
Audit & Observability
Every prompt, decision, approval, and action appended to immutable audit log. Prometheus metrics on all services
◈ TECHNOLOGY STACK
PLATFORM
OBSERVABILITY
AI / AGENT
AUTOMATION
SECURITY
CI/CD
◈ BUSINESS OUTCOMES
Reduces MTTR
50%+ reduction in mean time to resolve in simulated incidents
Cuts alert noise
40%+ via deduplication and grouping of related alerts
Autonomous resolution
60%+ of common incidents resolved without manual shell access
Auditability
Every prompt, decision, approval, and action logged with full traceability
Human control preserved
High-risk actions require explicit approval. Agent cannot call arbitrary tools.
Policy-gated execution
Allowlist-only actions. Blocked namespaces. Confidence thresholds enforced.
◈ READY TO EXPLORE
See it in action
Open the live incident dashboard to see AI triage, confidence scoring, and remediation decisions on simulated Kubernetes incidents.
OPEN DASHBOARD →