European ASP.NET Core 10.0 Hosting - HostForLIFE :: Using ASP.NET Core to Create AI-Powered Incident Postmortem Generators

July 2, 2026 09:02 by author

In contemporary software systems, production mishaps are unavoidable. Every engineering team ultimately encounters problems that affect customers and company operations, whether it's a botched deployment, a database outage, a disruption to a third-party API, or an unforeseen application fault.

Learning from incidents is just as important as resolving them. Incident postmortems are crucial in this situation. A well-written postmortem aids teams in comprehending what transpired, determining the underlying causes, recording lessons learned, and averting future occurrences of the same kind.

Unfortunately, the process of preparing postmortems is sometimes labor-intensive and laborious. Before creating a thorough report, engineers must compile logs, timelines, monitoring data, deployment history, and incident notes. Consequently, postmortems are often postponed, left unfinished, or omitted entirely.

By autonomously gathering event data, creating timelines, determining likely root causes, and creating organized postmortem reports, artificial intelligence might greatly enhance this procedure.

In this article, we'll use ASP.NET Core, OpenTelemetry, Azure OpenAI, and Application Insights to create an AI-powered incident postmortem generator.

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

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:

Monitoring tools
Log management platforms
Incident management systems
Deployment pipelines
Team communication channels
Ticketing systems

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:

Application Insights
OpenTelemetry
GitHub Actions
Azure DevOps
Incident Management Systems
Monitoring Platforms

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:

Faster onboarding
Better operational knowledge
Improved troubleshooting

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
Building dependable software systems requires incident postmortems, but producing them by hand can be laborious and inconsistent. Engineering teams may automatically gather incident data, recreate timelines, identify core causes, and produce structured reports with practical suggestions with the use of AI-powered postmortem generators.

Organizations may change incident management from a reactive process to a continuous learning system by integrating ASP.NET Core, OpenTelemetry, Application Insights, and Azure OpenAI. Automated postmortem generation will become a regular feature for contemporary DevOps and Site Reliability Engineering teams as AI-driven observability develops.

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