DEV Community: Muhammad Lutfi Muzaki

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

Muhammad Lutfi Muzaki — Sat, 08 Aug 2026 18:25:56 +0000

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! 🚀🦀

Beyond Autocomplete: Meta Muse Code, AWS Kiro, and the Rise of Multi-Agent AI Planning 🤖⚡

Muhammad Lutfi Muzaki — Sat, 08 Aug 2026 18:20:06 +0000

Remember when "AI coding" just meant inline tab-completion suggesting a for loop in VS Code?

Those were simpler times. 😅

Fast forward to this week, and we’ve officially crossed the threshold into the Autonomous Multi-Agent Era. The industry is shifting away from single-turn autocomplete prompts toward async, parallelized agentic workflows that inspect, plan, write, test, and validate code across entire repositories.

Three major developments dropped almost simultaneously:

⚡ Meta launched Muse Code (powered by Muse Spark 1.2) in beta, introducing parallel sub-agent execution.
☁️ AWS added an Agentic Workspace to Kiro, enabling async background task delegation for developers.
🎓 New Academic Research surfaced on how AI coding agents leverage structured "Agent Plans" for full-lifecycle repo maintenance, design, construction, testing, and validation.

Let's break down why this is a massive engineering paradigm shift and what it actually means for our daily developer workflows.

🧬 1. Meta Muse Code & Parallel Sub-Agent Swarms

Meta’s latest drop—Muse Code, driven by their Muse Spark 1.2 model—takes aim at one of the biggest bottlenecks in single-agent LLM systems: context dilution and linear execution delays.

When you ask a traditional LLM to refactor a complex microservice, it processes everything sequentially. It reads your files, thinks, writes code, tries to debug, and eventually runs out of context space or hits token output limits.

How Parallel Sub-Agent Execution Changes the Game

Muse Code doesn't just run one linear chat session. Instead, a primary orchestrator agent decomposes a high-level goal into specialized sub-agents running concurrently:

                     ┌──────────────────────────────┐
                     │   PRIMARY ORCHESTRATOR AGENT │
                     └──────────────┬───────────────┘
                                    │
         ┌──────────────────────────┼──────────────────────────┐
         ▼                          ▼                          ▼
┌─────────────────┐        ┌─────────────────┐        ┌─────────────────┐
│ SUB-AGENT A     │        │ SUB-AGENT B     │        │ SUB-AGENT C     │
│ AST Parsing &   │        │ Unit Test Suite │        │ Static Analysis │
│ Dependency Graph│        │ Generation      │        │ & Security Audit│
└────────┬────────┘        └────────┬────────┘        └────────┬────────┘
         │                          │                          │
         └──────────────────────────┼──────────────────────────┘
                                    ▼
                     ┌──────────────────────────────┐
                     │ CONSOLIDATED PR & DIFF RUN   │
                     └──────────────────────────────┘

Sub-Agent A analyzes static AST tree boundaries and imports.
Sub-Agent B drafts unit tests and edge-case mocks in parallel.
Sub-Agent C performs security linting and type checks on the proposed diff.

By isolating tasks into specialized sub-agent workers, Muse Code minimizes context noise, speeds up execution by orders of magnitude, and handles complex multi-file refactoring without choking.

☁️ 2. AWS Kiro’s Agentic Workspace: Going Asynchronous

If you’ve used synchronous AI pair programmers, you know the pain: you issue a complex prompt (e.g., "Migrate this service from REST to gRPC and update all DTO schemas"), and then you sit there staring at a spinning loader for 2 minutes while your IDE is effectively locked up.

AWS solved this anti-pattern by adding an Agentic Workspace to Kiro.

The Async Developer Workflow

Instead of blocking your active session, Kiro allows you to offload tasks asynchronously into a background workspace:

Dispatch & Detach: You send a background task: "Refactor the auth crate to use OAuth2 PKCE flow and fix broken integration tests."
Context Switching: You switch git branches and keep hacking on your primary feature.
Background Execution: Kiro’s background agent spins up an isolated sandbox, checks out the codebase, modifies code, runs build commands (cargo test, npm test), and self-corrects any compiler errors.
Push & Notify: When finished, you receive a notification with a ready-to-review branch diff complete with build pass/fail telemetry.

This shifts AI from a "chat overlay" to an asynchronous background engineering peer.

📜 3. The Research Blueprint: Structured "Agent Plans"

Alongside these commercial releases, landmark academic research published this week provided the theoretical blueprint for why these systems actually work on production codebases.

The paper highlights that unconstrained LLMs fail on real-world repositories because they lack deterministic structure. To solve this, advanced AI coding agents utilize a formal Agent Plan loop across 5 core software engineering phases:

┌──────────────┐     ┌──────────────┐     ┌──────────────┐     ┌──────────────┐     ┌──────────────┐
│ MAINTENANCE  │ ──► │    DESIGN    │ ──► │ CONSTRUCTION │ ──► │   TESTING    │ ──► │  VALIDATION  │
└──────────────┘     └──────────────┘     └──────────────┘     └──────────────┘     └──────────────┘

🔍 Maintenance & Reconnaissance: Mapping graph topologies, reading AGENTS.md / SKILL.md rules, inspecting ASTs, and verifying logs before making any edits.
📐 Architectural Design: Writing explicit implementation plans, identifying breaking API contract changes, and mapping component dependencies as Directed Acyclic Graphs (DAGs).
🏗️ Construction: Executing non-contiguous edits incrementally while preserving original docstrings and API signatures.
🧪 Automated Testing: Running test suites, interpreting compiler tracebacks, and diagnosing root causes rather than patching symptoms.
✅ Validation & Verification: Running static type checkers, verifying benchmark regressions, and generating clear post-mortem walkthroughs.

💡 What This Means for Us as Software Engineers

Are developers being replaced by these multi-agent swarms? Far from it.

What's actually happening is a fundamental shift in our role abstraction level:

Old Way: Spending 70% of our time typing syntax, boilerplate wiring, and debugging missing imports manually.
New Way: Acting as High-Level Software Architects & Agent Systems Engineers—defining precise boundary requirements, architectural guardrails, verification pipelines, and reviewing agent-generated pull requests.

The developers who thrive in 2026 won't be those who type syntax the fastest, but those who excel at system decomposition, architecture design, and orchestrating agent workflows.

💭 Over to You!

How is your team adapting to the agentic AI wave?

Are you still relying on single inline autocomplete, or have you integrated async background agents into your daily git workflow?
What’s your take on Meta's parallel sub-agent approach vs. AWS’s async workspace?

Drop your thoughts in the comments below! 👇💬