Introduction
Production incidents are inevitable in modern software systems. Whether it's a failed deployment, a database outage, a third-party API disruption, or an unexpected application bug, every engineering team eventually faces incidents that impact users and business operations.
While resolving incidents is critical, learning from them is equally important. This is where incident postmortems play a vital role. A well-written postmortem helps teams understand what happened, identify root causes, document lessons learned, and prevent similar incidents in the future.
Unfortunately, creating postmortems is often a manual and time-consuming process. Engineers must gather logs, timelines, monitoring data, deployment history, and incident notes before compiling a comprehensive report. As a result, postmortems are frequently delayed, incomplete, or skipped altogether.
Artificial Intelligence can significantly improve this process by automatically collecting incident data, generating timelines, identifying probable root causes, and producing structured postmortem reports.
In this article, we'll build an AI-powered incident postmortem generator using ASP.NET Core, OpenTelemetry, Azure OpenAI, and Application Insights.
Why Incident Postmortems Matter
The purpose of a postmortem is not to assign blame.
Instead, it helps organizations:
Understand what happened
Identify root causes
Improve operational processes
Prevent recurring incidents
Share institutional knowledge
Improve system reliability
A high-quality postmortem typically answers the following questions:
What happened?
When did it happen?
What systems were affected?
What was the business impact?
What caused the issue?
How was the issue resolved?
What actions will prevent recurrence?
AI can automate much of this process.
Challenges with Traditional Postmortems
Many organizations struggle with postmortem creation because incident information is scattered across multiple systems.
Data often resides in:
Engineers spend significant time gathering and organizing this information before they can even begin writing the report.
AI-powered automation reduces this effort dramatically.
Solution Architecture
A modern incident postmortem generator consists of several layers.
Data Sources
Collect incident information from:
Processing Layer
ASP.NET Core services normalize and aggregate incident data.
AI Analysis Layer
Azure OpenAI generates incident summaries, timelines, root causes, and recommendations.
Reporting Layer
Postmortems are published to:
Internal Wikis
SharePoint
Confluence
Email Reports
Incident Dashboards
Creating the ASP.NET Core Project
Create a new Web API project.
dotnet new webapi -n IncidentPostmortemGenerator
Install required packages.
dotnet add package Azure.AI.OpenAI
dotnet add package OpenTelemetry.Extensions.Hosting
dotnet add package Microsoft.ApplicationInsights.AspNetCore
These packages enable telemetry collection and AI integration.
Designing the Incident Model
Create a model that represents incident data.
public class IncidentRecord
{
public string IncidentId { get; set; }
public DateTime StartTime { get; set; }
public DateTime EndTime { get; set; }
public string Summary { get; set; }
public string RootCause { get; set; }
public List<string> Logs { get; set; }
}
This model becomes the foundation for AI analysis.
Collecting Incident Telemetry
Modern applications generate large amounts of telemetry.
Configure OpenTelemetry.
builder.Services.AddOpenTelemetry()
.WithTracing(builder =>
{
builder.AddAspNetCoreInstrumentation();
builder.AddHttpClientInstrumentation();
});
Telemetry data may include:
Request traces
Error logs
Dependency failures
Database exceptions
Performance metrics
These signals help reconstruct incident timelines.
Capturing Deployment Events
Many incidents occur shortly after deployments.
Store deployment information alongside incident data.
public class DeploymentEvent
{
public string Version { get; set; }
public DateTime DeploymentTime { get; set; }
public string CommitHash { get; set; }
}
This allows AI to correlate incidents with release activity.
Building the AI Postmortem Service
Create a service that generates postmortem reports.
public class PostmortemGeneratorService
{
private readonly OpenAIClient _client;
public PostmortemGeneratorService(
OpenAIClient client)
{
_client = client;
}
public async Task<string> GenerateAsync(
IncidentRecord incident)
{
var prompt = $"""
Generate an incident postmortem.
Incident:
{incident.Summary}
Root Cause:
{incident.RootCause}
Logs:
{string.Join("\n", incident.Logs)}
Include:
1. Executive Summary
2. Timeline
3. Impact Analysis
4. Root Cause
5. Resolution
6. Action Items
""";
var response =
await _client.GetChatCompletionsAsync(
"gpt-4o",
new ChatCompletionsOptions
{
Messages =
{
new ChatMessage(
ChatRole.User,
prompt)
}
});
return response.Value
.Choices[0]
.Message
.Content;
}
}
The AI model transforms raw incident data into a structured report.
Example AI-Generated Postmortem
Input:
Incident:
Checkout Service Failure
Root Cause:
Database Connection Pool Exhaustion
Duration:
45 Minutes
Generated output:
Executive Summary:
Users experienced checkout failures due to
database connection pool exhaustion.
Impact:
32% of transactions failed.
Root Cause:
Increased traffic combined with insufficient
connection pool configuration.
Resolution:
Connection pool size increased and service restarted.
Action Items:
- Review database capacity planning.
- Implement connection monitoring.
This saves significant time during incident reviews.
Generating Incident Timelines
One of the most valuable postmortem sections is the timeline.
AI can automatically create a chronological sequence of events.
Example:
09:05 AM - Deployment completed
09:10 AM - Error rates increased
09:15 AM - Alert triggered
09:18 AM - Incident declared
09:45 AM - Root cause identified
09:55 AM - Fix deployed
10:00 AM - Service restored
This helps stakeholders understand the progression of events.
Automated Impact Analysis
AI can estimate incident impact using telemetry.
Example metrics:
Affected Users:
18,000
Failed Requests:
245,000
Revenue Impact:
Estimated Moderate
Severity:
High
This provides valuable business context.
Root Cause Correlation
AI can analyze:
Deployment history
Error logs
Trace data
Infrastructure metrics
to identify probable causes.
Example:
Most Likely Cause:
Recent deployment introduced inefficient
database queries resulting in resource exhaustion.
These insights accelerate learning and remediation.
Creating Action Items Automatically
A postmortem is only useful if it leads to improvements.
AI can generate recommendations such as:
Action Items:
1. Implement connection pool monitoring.
2. Add load testing before deployments.
3. Configure automatic scaling.
4. Improve alert thresholds.
These recommendations help prevent future incidents.
Advanced Enterprise Features
Large organizations often extend postmortem generation with additional capabilities.
Multi-Service Incident Analysis
Correlate incidents across:
APIs
Databases
Kubernetes clusters
Message queues
to generate complete reports.
Historical Incident Comparison
Compare new incidents against past events.
Example:
Similar Incident:
INC-2025-102
Similarity Score:
87%
This helps teams identify recurring patterns.
Knowledge Base Integration
Store generated postmortems in searchable repositories.
Benefits include:
Executive Summaries
Generate non-technical summaries for leadership teams.
This improves communication across the organization.
Best Practices
Collect High-Quality Telemetry
The quality of AI-generated reports depends on the quality of input data.
Invest in logging, monitoring, and tracing.
Standardize Incident Metadata
Capture:
Severity
Duration
Impact
Resolution
for every incident.
Validate AI Output
Engineers should review reports before publishing them.
Store Historical Reports
Past incidents provide valuable learning opportunities.
Focus on Continuous Improvement
Use postmortems to improve systems rather than assign blame.
Benefits of AI-Powered Postmortem Generation
Organizations implementing automated postmortem systems often achieve:
Faster incident documentation
Reduced operational overhead
Better knowledge sharing
Improved reliability engineering
Consistent reporting standards
Increased engineering productivity
Teams spend less time writing reports and more time improving systems.
Conclusion
Incident postmortems are essential for building reliable software systems, but creating them manually can be time-consuming and inconsistent. AI-powered postmortem generators help engineering teams automatically collect incident data, reconstruct timelines, analyze root causes, and generate structured reports with actionable recommendations.
By combining ASP.NET Core, OpenTelemetry, Application Insights, and Azure OpenAI, organizations can transform incident management from a reactive process into a continuous learning system. As AI-driven observability continues to evolve, automated postmortem generation will become a standard capability for modern DevOps and Site Reliability Engineering teams.
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