Building AI-Powered Operational Runbook Generation Systems with ASP.NET Core

Introduction

Modern cloud-native systems have become increasingly complex. Organizations operate hundreds of microservices, Kubernetes clusters, databases, message brokers, APIs, AI services, and third-party integrations across multiple environments.

When incidents occur, engineering teams depend heavily on operational runbooks to diagnose problems, execute recovery procedures, and restore service availability.

Unfortunately, maintaining runbooks is often one of the most neglected operational activities.

Many organizations struggle with:

Missing runbooks
Outdated procedures
Inconsistent documentation
Tribal knowledge trapped within teams
Slow incident response
Difficult onboarding processes

As infrastructure evolves, runbooks quickly become outdated, reducing their effectiveness during critical incidents.

Artificial Intelligence can analyze infrastructure configurations, deployment pipelines, incident history, monitoring systems, operational logs, and technical documentation to automatically generate and continuously update operational runbooks.

In this article, we'll build an AI-powered Operational Runbook Generation System using ASP.NET Core, OpenTelemetry, Azure Monitor, Azure DevOps, vector search, and Azure OpenAI.

What Is an Operational Runbook?

An operational runbook is a documented procedure that guides engineers through operational tasks.

Examples include:

Service restart procedures
Incident response workflows
Database recovery processes
Disaster recovery operations
Deployment rollback procedures
Infrastructure troubleshooting

Example:

Issue:
API Service Unavailable

Step 1:
Check Kubernetes Pods

Step 2:
Review Application Logs

Step 3:
Restart Failed Pods

Runbooks help teams respond consistently during incidents.

Why Traditional Runbooks Become Obsolete

Many runbooks are written manually.

Over time:

Infrastructure changes
Services evolve
Architectures grow
Procedures change

Example:

Original Runbook:
Restart VM

Current architecture:

Application Now Runs
on Kubernetes

The runbook is no longer accurate.

AI can continuously adapt runbooks to evolving systems.

Common Runbook Challenges

Organizations frequently encounter similar problems.

Missing Documentation

Critical procedures are undocumented.

Knowledge Silos

Only a few engineers understand recovery processes.

Outdated Procedures

Documentation no longer reflects reality.

Slow Incident Response

Engineers spend valuable time investigating instead of resolving.

Inconsistent Execution

Different responders follow different processes.

AI helps standardize operational knowledge.

How AI Improves Runbook Management

AI can analyze:

Infrastructure definitions
Monitoring data
Incident reports
Service dependencies
Operational logs
Historical resolutions

Example output:

Incident:
High API Latency

Generated Runbook:

1. Check database latency
2. Review cache hit ratio
3. Analyze slow queries
4. Scale API replicas

This provides actionable operational guidance.

Solution Architecture

An AI-powered runbook platform consists of four layers.

Knowledge Collection Layer

Gather information from:

Azure Monitor
Application Insights
GitHub
Azure DevOps
Infrastructure Repositories

Incident Intelligence Layer

Analyze incidents and resolutions.

AI Generation Layer

Create runbooks automatically.

Knowledge Repository Layer

Store and version generated runbooks.

Creating the ASP.NET Core Project

Create a new project.

dotnet new webapi -n RunbookGenerator

Install required packages.

dotnet add package Azure.AI.OpenAI
dotnet add package OpenTelemetry.Extensions.Hosting
dotnet add package Azure.Monitor.Query

These packages provide telemetry and AI capabilities.

Designing the Runbook Model

Create a runbook model.

public class OperationalRunbook
{
    public string Title { get; set; }

    public string IncidentType { get; set; }

    public List<string> Steps { get; set; }

    public string RecoveryCriteria { get; set; }
}

This model represents generated runbooks.

Capturing Incident Information

Create an incident model.

public class IncidentRecord
{
    public string IncidentType { get; set; }

    public string RootCause { get; set; }

    public string Resolution { get; set; }
}

Historical incidents provide valuable training data.

Collecting Telemetry Data

Configure OpenTelemetry.

builder.Services
    .AddOpenTelemetry()
    .WithTracing(builder =>
    {
        builder.AddAspNetCoreInstrumentation();
        builder.AddHttpClientInstrumentation();
    });

Operational telemetry provides real-time context.

Building the AI Runbook Engine

Create a runbook generation service.

public class RunbookGenerationService
{
    private readonly OpenAIClient _client;

    public RunbookGenerationService(
        OpenAIClient client)
    {
        _client = client;
    }

    public async Task<string> GenerateAsync(
        string incidentData)
    {
        var prompt = $"""
        Generate an operational runbook.

        Include:

        1. Symptoms
        2. Diagnostic Steps
        3. Recovery Procedures
        4. Validation Steps
        5. Escalation Criteria

        {incidentData}
        """;

        var response =
            await _client.GetChatCompletionsAsync(
                "gpt-4o",
                new ChatCompletionsOptions
                {
                    Messages =
                    {
                        new ChatMessage(
                            ChatRole.User,
                            prompt)
                    }
                });

        return response.Value
            .Choices[0]
            .Message
            .Content;
    }
}

The AI engine transforms operational knowledge into structured runbooks.

Example AI-Generated Runbook

Input:

Incident:
Database Connection Failures

Environment:
Production

Generated output:

Symptoms:
Database timeout errors

Diagnostics:
Check connection pool metrics

Recovery:
Restart database proxy

Validation:
Confirm successful transactions

This provides a consistent operational procedure.

Generating Incident-Specific Runbooks

AI can create targeted runbooks for specific scenarios.

Example:

Issue:
High CPU Usage

Generated runbook:

1. Review top CPU consumers

2. Check deployment activity

3. Analyze application traces

4. Scale workload if necessary

This improves operational efficiency.

Learning from Historical Incidents

Past incidents provide valuable operational knowledge.

Example:

Previous Incident:
Kubernetes Node Failure

Resolution:
Node replacement

AI can incorporate successful resolutions into future runbooks.

Generated insight:

Recommended Recovery Procedure:
Automated node replacement workflow.

This enables continuous improvement.

Dependency-Aware Runbooks

Service dependencies often influence recovery procedures.

Example:

Order Service
      ↓
Payment Service
      ↓
SQL Database

AI can generate dependency-aware troubleshooting workflows.

Example output:

Check Database Health
Before Restarting Payment Service.

This reduces operational mistakes.

Automated Escalation Guidance

Knowing when to escalate is critical.

Example:

Service Downtime:
30 Minutes

Affected Customers:
15,000

AI recommendation:

Escalation Level:
Critical Incident Response Team

This improves incident management.

Recovery Validation Procedures

Recovery is not complete until systems are verified.

Example:

Recovery Actions:
Completed

Generated validation checklist:

1. Verify API health

2. Confirm database connectivity

3. Validate customer transactions

4. Review error metrics

This improves reliability.

Continuous Runbook Updates

One of AI's most valuable capabilities is keeping runbooks current.

Example:

Infrastructure Change:
Migration to Kubernetes

AI recommendation:

Update Existing Runbook:
Replace VM restart steps
with Kubernetes deployment procedures.

This prevents documentation drift.

Intelligent Onboarding Assistance

Runbooks can also support new engineers.

Example:

Experience Level:
Junior Engineer

AI-generated guidance:

Include additional explanations,
links to dashboards,
and troubleshooting examples.

This accelerates onboarding.

Advanced Enterprise Features

Large organizations often enhance runbook platforms with additional capabilities.

Incident Prediction Integration

Generate runbooks before incidents occur.

ChatOps Integration

Surface runbooks directly in Teams or Slack.

Multi-Cloud Support

Generate procedures across Azure, AWS, and Kubernetes.

Compliance Validation

Ensure operational procedures meet governance requirements.

Executive Reporting

Generate operational readiness dashboards.

Best Practices

Version Control Runbooks

Track operational procedure changes over time.

Continuously Validate Procedures

Ensure runbooks remain accurate.

Capture Incident Learnings

Use postmortems to improve generated content.

Automate Telemetry Collection

Accurate telemetry improves AI recommendations.

Review AI-Generated Content

Platform and operations teams should validate runbooks before production use.

Benefits of AI-Powered Runbook Generation

Organizations implementing intelligent runbook systems often achieve:

Faster incident response
Reduced operational risk
Improved onboarding
Better documentation quality
Greater consistency
Reduced knowledge silos

Teams spend less time searching for information and more time resolving incidents.

Conclusion

Operational runbooks are essential for maintaining reliable systems, yet creating and maintaining them manually is often difficult in rapidly evolving cloud environments. As architectures become increasingly distributed, keeping operational knowledge current becomes a significant challenge.

By combining ASP.NET Core, OpenTelemetry, Azure Monitor, incident intelligence, vector search, and Azure OpenAI, organizations can build AI-powered runbook generation platforms that automatically create, maintain, and improve operational procedures. As platform engineering continues to mature, intelligent runbook generation will become a critical capability for achieving operational excellence and reducing incident resolution times.