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Introduction

Welcome to the CI/CD for Non-Agentic Workloads learning path. In this module, you will add observability to a traditional Azure provisioning pipeline — without modifying any application code — using Monocle's zero-code YAML configuration. When the pipeline fails, an AI-powered SRE agent automatically analyzes the traces and provides root cause analysis.

What you will learn

  • How to instrument any Python application with zero code changes using okahu.yaml
  • How Monocle captures function inputs, outputs, and errors as trace spans
  • How to build a CI/CD workflow that triggers AI-powered root cause analysis on failure
  • How the Kahu SRE Agent uses traces to diagnose deployment failures

Scenario

You are running a 4-step Azure provisioning pipeline that deploys Blob Storage, SQL Database, Kusto tables, and user accounts. Step 4 fails. Instead of manually digging through logs, you want the CI/CD pipeline to automatically send traces to an AI agent that diagnoses the root cause and posts the analysis to a GitHub issue.

Architecture overview

graph TD
    GHA[GitHub Actions] --> Monocle[Monocle Zero-Code Runner]
    Monocle --> App[deploy_app.py]
    App --> S1[Step 1: Azure Blob]
    App --> S2[Step 2: Azure SQL]
    App --> S3[Step 3: Kusto Tables]
    App --> S4[Step 4: User Accounts ❌]
    Monocle --> File[.monocle/ traces]
    Monocle --> Cloud[Okahu Cloud]
    S4 -->|Failure| GHA
    GHA --> Query[Query Okahu for Traces]
    Query --> SRE[Kahu SRE Agent]
    SRE --> Issue[GitHub Issue with RCA]

Source code

okahu-demos/cicd_azure_provisioning

Time to complete

~30 minutes