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AIOps 101

A hands-on series on building AIOps capabilities using Python, Kubernetes, LLMs, and the tools I actually use in my personal projects. Every article draws directly from my open-source project simple-ai-agentarrow-up-right — a production-ready AI agent that connects Telegram and Slack to Kubernetes cluster management, automated remediation, and LLM-powered root cause analysis.

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What This Series Is About

AIOps gets thrown around a lot as a buzzword. In practice, it means using AI and automation to reduce the toil of operating software systems — catching incidents before humans notice, correlating alerts into coherent events, and executing remediation steps with appropriate human oversight.

This series is not about the buzzword. It's about the engineering: what the components are, how they wire together, what the failure modes look like in real clusters, and how to implement them from scratch using tools you likely already have.

I built simple-ai-agent as a personal project to manage my own Kubernetes homelab through Telegram and Slack. What started as a chat bot that could list pods grew into a full AIOps engine — a background watch-loop detecting CrashLoopBackOff and OOMKilled events, a YAML rule engine, playbook-based remediation with risk gating, and an LLM that writes SRE-quality root cause analysis reports. This series documents that journey and the design decisions behind every component.

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Project Reference

All code examples, architecture diagrams, and configuration snippets in this series reference the actual implementation at:

github.com/Htunn/simple-ai-agentarrow-up-right

The project is MIT-licensed. You can run it locally, adapt it for your own infrastructure, and follow along with real code rather than toy examples.

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Who This Series Is For

  • SREs and platform engineers who manage Kubernetes and want to reduce on-call toil

  • Backend engineers curious how LLMs integrate with operational tooling

  • Hobbyists running a homelab and want something smarter than static Prometheus alerts

  • Anyone building their own AI agent and wanting to extend it toward operations

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Prerequisites

  • Comfortable with Python (async/await, Pydantic, FastAPI basics)

  • Basic Kubernetes knowledge (pods, deployments, events, kubectl)

  • Familiarity with Prometheus and the concept of metrics/alerts

  • A Kubernetes cluster to experiment against (local kind or minikube works fine)

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Series Structure

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Phase 1: Foundations (Articles 1–2)

Understanding what AIOps means in practice and how the overall system is architected.

  1. What is AIOps — and Why I Built One

    • The gap between Prometheus alerts and actual resolution

    • What an AIOps system actually does (watch, detect, decide, act)

    • My goals when starting simple-ai-agent

    • A tour of the final system

  2. Architecture and Stack Decisions

    • Layered architecture: Channel → API → Business Logic → AIOps → Data

    • Why FastAPI, PostgreSQL, Redis for this problem

    • MCP (Model Context Protocol) for tool execution

    • How the chat channels (Telegram/Slack) connect to the operations backend

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Phase 2: The AIOps Engine (Articles 3–6)

Building the four core components — watch-loop, rule engine, playbook executor, and RCA engine.

  1. The Watch-Loop — Continuous Cluster Health Polling

    • The background polling pattern and why pull beats push for homelab use

    • What cluster events to watch for: CrashLoopBackOff, OOMKilled, NotReady nodes, zero replicas

    • ClusterEvent data model design

    • Graceful startup, shutdown, and error containment

  2. The Rule Engine — Turning Events into Actionable Alerts

    • YAML-defined alert rules and severity mapping

    • Pattern matching against ClusterEvent objects

    • Avoiding alert storms: deduplication and cooldown windows

    • Real rule examples from config/alert_rules.yml

  3. Playbooks and Human-in-the-Loop Approvals

    • Ordered remediation step sequences

    • Risk levels: LOW (auto-execute) / MEDIUM (gate on approval) / HIGH (warn only)

    • Redis-backed approval manager with TTL expiry

    • Chat-native approve / reject pattern

    • Why full auto-remediation is almost always wrong

  4. LLM-Powered Root Cause Analysis

    • Designing the SRE prompt for structured JSON output

    • Anthropic API: claude-3-5-sonnet-20241022

    • Confidence scoring and evidence extraction

    • Integrating RCA reports into the chat response

    • Hallucination mitigation: grounding the LLM with real cluster data

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Phase 3: Integration and Observability (Articles 7–8)

Wiring the AIOps engine into the production alerting pipeline and making the system itself observable.

  1. Alertmanager Integration — Bringing Prometheus Alerts into the Agent

    • POST /api/alert/webhook receiver design

    • Alertmanager routing configuration (alertmanager.yml)

    • Translating Prometheus alert labels into ClusterEvent objects

    • Alert deduplication and resolved-alert handling

    • End-to-end flow: metric → Prometheus rule → Alertmanager → agent → playbook

  2. Observability — Monitoring Your AIOps Agent

    • structlog JSON logging and why it matters for alert correlation

    • Prometheus metrics from the agent itself

    • Health endpoint design: subsystem-level status

    • Grafana dashboard setup

    • Debugging the watch-loop and approval flow

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How to Follow Along

Clone the reference project before starting:

Each article references specific files in the repository using paths like src/aiops/watchloop.py, so you can read the real implementation alongside the explanation.

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Series File Structure

Ready? Start with Article 1: What is AIOps.

PreviousPart 5: Production PatternsNextArticle 1: What is AIOps?

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