Stop Chasing Symptoms: How We Built an Autonomous Root Cause Analysis Engine in Rust 🦀

It’s 2:15 AM. Your phone buzzes aggressively. 🚨 You jump out of bed, open your laptop with half-closed eyes, and join an emergency incident response call. Your team’s Slack channel is exploding: - ⚠️ [ALERT] Payment API 500 Error Rate > 15% - ⚠️ [ALERT] Redis Latency Timeout (>5000ms) - ⚠️ [ALERT] Node-04 CPU Saturation (98%) You spend the next 2 hours manually connecting the dots: querying Prometheus metrics, scrolling through endless Loki logs, cross-referencing Tempo traces, and checking recent ArgoCD deployments. Eventually, you uncover the truth: Deployment #218, pushed right before midnight, introduced a subtle memory leak that triggered GC pressure, spiked CPU, starved the Redis connection pool, and knocked down the Payment API. Sounds familiar? 😅 💥 The Problem: Observability Shows Symptoms, Not Causes Modern observability tools like Grafana, Prometheus, Loki, and Jaeger are fantastic at collecting metrics, logs, and traces. But they suffer from one fundamental design limitation: They tell you WHAT is breaking, but leave you to figure out WHY it broke. When a microservice fails in Kubernetes, it triggers a domino effect (cascading failure): Deployment #218 (Memory Leak) │ ▼ Garbage Collection Pressure │ ▼ CPU Saturation (98%) │ ▼ Redis Connection Timeout │ ▼ API Gateway Retry Storm │ ▼ Payment Service Down (HTTP 500) Traditional alerting floods you with alerts for the bottom 4 nodes (the symptoms), leaving SREs and DevOps engineers stuck sifting through noise during high-stakes outages. 💡 Introducing IRCAE: Autonomous Root Cause Engine To solve this, we are building IRCAE (Intelligent Root Cause Analysis Engine)-an open-source, enterprise-grade platform designed to turn raw telemetry into autonomous causal reasoning. Instead of asking SREs to correlate telemetry manually, IRCAE automatically answers: "Why did the system fail?" in less than 10 seconds. 🌟 Key Highlights - 🚀 Written in Rust (Axum + Tokio): Built for high-throughput, near-bare-metal performance with zero garbage collection pauses. - 🕸️ Dynamic Multi-Layer Knowledge Graph: Automatically maps service dependencies, Kubernetes pods, nodes, git commits, and cloud infrastructure. - 🧮 Mathematical Causal Inference (SCM & Bayesian Networks): Deterministic, hallucination-free causal algorithms (PyTorch Geometric GNN / TGN). - 📝 Explainable AI (XAI): LLMs are only used at the very last step to translate structured mathematical proofs into human-readable incident post-mortems! ⚙️ How IRCAE Works Under the Hood IRCAE processes millions of telemetry events per minute through a clean 4-stage pipeline: 1. TELEMETRY INGESTION (Prometheus, Loki, OTel, K8s, Git) │ ▼ 2. TOPOLOGY GRAPH DISCOVERY (Service & Infra Dependency Graph) │ ▼ 3. CAUSAL REASONING ENGINE (Structural Causal Models & DBN) │ ▼ 4. EVIDENCE RANKING & POST-MORTEM GENERATION (< 10 seconds) 1️⃣ Telemetry Ingestion & Correlation IRCAE ingests metrics (Prometheus/VictoriaMetrics), logs (Loki/Elastic), traces (Jaeger/OTel), and infrastructure events (Kubernetes API, ArgoCD, GitHub webhooks) into a synchronized temporal sliding window. 2️⃣ Dynamic Topology Discovery Using trace headers and Kubernetes metadata, IRCAE constructs a dynamic graph: - Nodes: Services, Pods, Nodes, Commit SHAs, Database Instances. - Edges: CALLS ,RUNS_ON ,DEPLOYED_BY ,DEPENDS_ON . 3️⃣ Hallucination-Free Causal Reasoning Unlike "AI Ops" tools that throw raw logs directly at an LLM (leading to wild hallucinations), IRCAE relies on strict mathematical models: - Structural Causal Models (SCM): Formulates variables as $Y = f(X, U)$. - Dynamic Bayesian Networks: Computes $P(\text{RootCause} \mid \text{ObservedAnomalies})$. 4️⃣ Ranked Evidence Output IRCAE outputs ranked hypotheses with concrete confidence scores and supporting evidence: { "incident_id": "inc-2026-0807-001", "confidence_score": 0.965, "primary_root_cause": { "type": "DEPLOYMENT_MEMORY_LEAK", "target_entity": "deployment/payment-service", "commit_sha": "8f2a1c9b" }, "evidence": [ "Deployment v2.1.8 occurred at 14:00 UTC", "Pod memory increased by +420%", "Redis connection pool exhausted at 14:03 UTC" ] } ⚡ Quick Start: Analyzing an Incident via REST API Because IRCAE is written in Rust, running an analysis is lightning fast: curl -X POST http://localhost:8080/api/v1/incidents/analyze \ -H "Content-Type: application/json" \ -d '{ "title": "Payment Gateway Timeout", "events": [ { "id": "ev-101", "source_system": "KubernetesAPI", "event_type": "Deployment", "entity_id": "deployment/payment-service", "timestamp": "2026-08-07T00:00:00Z", "anomaly_score": 0.95 }, { "id": "ev-102", "source_system": "Prometheus", "event_type": "MetricAnomaly", "entity_id": "pod/payment-pod-1", "timestamp": "2026-08-07T00:01:00Z", "anomaly_score": 0.75 } ] }' 🤝 What's Next & How to Get Involved Observability needs a paradigm shift from passive dashboards to autonomous root cause reasoning. We are actively developing IRCAE as an Apache-2.0 open-source project, and we’d love your feedback, contributions, and ideas! - ⭐️ GitHub Repo: muhammadlutfimuzaki/ircae (give us a star if you like the concept!) - 💬 Drop a comment below: How does your team currently handle cascading microservice failures during on-call incidents? Happy coding & zero-downtime shipping! 🚀🦀 Top comments (0)