Building AI-Powered Architecture Decision Record (ADR) Assistants with .NET

Introduction

As software systems evolve, engineering teams continuously make architectural decisions that influence scalability, maintainability, performance, security, and operational complexity. Decisions such as adopting microservices, selecting databases, implementing caching strategies, choosing messaging platforms, or integrating AI capabilities can have long-term consequences across an organization.

Unfortunately, many architectural decisions are never properly documented. Months later, engineers often ask:

Why did we choose this architecture?
What alternatives were considered?
Who approved the decision?
What trade-offs were identified?
Is the original decision still valid?

Without proper documentation, organizations lose valuable architectural knowledge, making onboarding, maintenance, and future decision-making more difficult.

Architecture Decision Records (ADRs) help solve this problem by documenting important architectural choices and their rationale. However, creating ADRs manually is often time-consuming, leading to incomplete or outdated documentation.

Artificial Intelligence can simplify ADR creation by analyzing project requirements, technical constraints, architecture discussions, repository activity, and historical decisions to automatically generate high-quality Architecture Decision Records.

In this article, we'll build an AI-powered ADR assistant using ASP.NET Core, Azure OpenAI, vector search, and repository intelligence.

What Is an Architecture Decision Record?

An Architecture Decision Record is a lightweight document that captures a significant architectural decision.

A typical ADR includes:

Context
Problem Statement
Decision
Alternatives Considered
Consequences
Status

Example:

Decision:
Use Redis for distributed caching.

Status:
Accepted.

The goal is to preserve architectural reasoning for future teams.

Why Teams Struggle with ADR Documentation

Many organizations recognize the value of ADRs but rarely maintain them consistently.

Common challenges include:

Time Constraints

Engineers prioritize feature delivery over documentation.

Missing Context

Important discussions happen in meetings, chats, and pull requests.

Inconsistent Formats

Different teams document decisions differently.

Outdated Records

Architecture evolves while documentation remains unchanged.

AI can automate much of this process.

How AI Improves ADR Management

AI can analyze:

Pull requests
GitHub discussions
Design documents
Project requirements
Technical specifications
Historical ADRs

Instead of writing ADRs manually, engineers receive generated drafts that require only minor review.

Example output:

Decision:
Adopt Azure Service Bus.

Reason:
Improved reliability and
support for asynchronous workflows.

Alternatives:
RabbitMQ
Apache Kafka

Status:
Proposed

This significantly reduces documentation effort.

Solution Architecture

An AI-powered ADR assistant consists of four layers.

Knowledge Collection Layer

Collect information from:

GitHub Repositories
Azure DevOps
Internal Wikis
Design Documents
Project Backlogs

Processing Layer

Extract architectural discussions and technical context.

AI Analysis Layer

Azure OpenAI generates ADR content.

Knowledge Repository Layer

Store ADRs in Git repositories or documentation portals.

Creating the ASP.NET Core Project

Create a new Web API project.

dotnet new webapi -n ADRAssistant

Install required packages.

dotnet add package Azure.AI.OpenAI
dotnet add package Octokit

These packages provide repository intelligence and AI integration.

Designing the ADR Model

Create a model representing an ADR.

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

    public string Context { get; set; }

    public string Decision { get; set; }

    public string Consequences { get; set; }

    public string Status { get; set; }
}

This model becomes the foundation for generated ADRs.

Collecting Architectural Context

Architectural decisions often originate from multiple sources.

Examples include:

Pull requests
Design reviews
Planning meetings
Technical RFCs

Create a context model.

public class ArchitecturalContext
{
    public string Requirement { get; set; }

    public string Constraint { get; set; }

    public string DiscussionSummary { get; set; }
}

This information provides valuable input for AI analysis.

Integrating Repository Intelligence

Git repositories contain architectural clues.

Examples:

Infrastructure changes
Database migrations
New service introductions
Dependency updates

GitHub integration example:

public class RepositoryAnalysisService
{
    private readonly GitHubClient _client;

    public RepositoryAnalysisService(
        string token)
    {
        _client = new GitHubClient(
            new ProductHeaderValue(
                "ADRAssistant"));

        _client.Credentials =
            new Credentials(token);
    }
}

Repository activity helps reconstruct decision history.

Building the AI ADR Generator

Create an AI service.

public class ADRGenerationService
{
    private readonly OpenAIClient _client;

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

    public async Task<string> GenerateAsync(
        string architectureData)
    {
        var prompt = $"""
        Generate an Architecture Decision Record.

        Include:

        1. Context
        2. Problem Statement
        3. Decision
        4. Alternatives
        5. Consequences
        6. Status

        {architectureData}
        """;

        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 technical discussions into structured ADRs.

Example AI-Generated ADR

Input:

Requirement:
Distributed caching

Traffic:
High

Cloud Platform:
Azure

Generated output:

Decision:
Adopt Azure Cache for Redis.

Alternatives:
SQL Cache
In-Memory Cache

Consequences:
Improved scalability and lower latency.

Status:
Accepted

This reduces documentation effort significantly.

Suggesting Architectural Alternatives

One valuable AI capability is alternative analysis.

Example:

Current Decision:
Azure Service Bus

Alternatives:
RabbitMQ
Apache Kafka
Azure Event Grid

AI can compare options and explain trade-offs.

Example output:

Recommended:
Azure Service Bus

Reason:
Managed infrastructure and
strong Azure ecosystem integration.

This improves decision quality.

Historical Decision Analysis

Organizations often make similar decisions repeatedly.

AI can analyze previous ADRs.

Example:

Related ADRs:
ADR-017
ADR-042
ADR-053

Generated insight:

Previous messaging decisions
consistently favored managed services
to reduce operational overhead.

This helps maintain architectural consistency.

Detecting ADR Gaps

Many architectural decisions never receive documentation.

AI can identify potential ADR candidates.

Examples include:

Database migrations
Framework upgrades
Cloud platform changes
Security architecture updates

Example output:

Potential Missing ADR:

Migration from SQL Server
to Azure Cosmos DB.

This improves governance and traceability.

ADR Search and Discovery

As ADR collections grow, finding relevant decisions becomes difficult.

AI-powered search enables queries such as:

Why did we choose Redis?

What decisions involve messaging systems?

Semantic search provides much better results than keyword matching.

Generating ADR Summaries

Executives and stakeholders often prefer concise summaries.

Example:

Decision:
Adopt Redis

Benefit:
Improved scalability

Risk:
Additional infrastructure cost

AI can generate summaries automatically for different audiences.

Advanced Enterprise Features

Large organizations often enhance ADR assistants with additional intelligence.

Architectural Consistency Analysis

Detect decisions that conflict with established standards.

Compliance Validation

Verify that decisions align with security and governance requirements.

Architecture Review Assistance

Generate discussion points before architecture review meetings.

Knowledge Graph Integration

Connect ADRs with services, repositories, teams, and infrastructure.

Architecture Impact Forecasting

Estimate long-term consequences of proposed decisions.

Best Practices

Keep ADRs Lightweight

Focus on decisions rather than excessive documentation.

Capture Alternatives

Understanding rejected options is often as valuable as the chosen solution.

Store ADRs with Source Code

Version-controlled ADRs remain accessible and auditable.

Review ADRs Periodically

Some decisions become outdated as systems evolve.

Use AI as a Drafting Assistant

Final decisions should still be reviewed by architects and engineering leaders.

Benefits of AI-Powered ADR Assistants

Organizations implementing intelligent ADR systems often achieve:

Better architectural documentation
Faster decision recording
Improved onboarding
Reduced knowledge loss
Greater architectural consistency
Enhanced governance

Teams spend less time writing documents and more time making informed decisions.

Conclusion

Architecture Decision Records play a critical role in preserving engineering knowledge, but maintaining them manually can be difficult in fast-moving organizations. As systems become more complex, the need for accurate and accessible architectural documentation continues to grow.

By combining ASP.NET Core, repository intelligence, vector search, historical decision analysis, and Azure OpenAI, organizations can build AI-powered ADR assistants that automatically generate, organize, and maintain architecture decisions. As AI becomes increasingly integrated into software engineering workflows, intelligent ADR management will become an essential component of modern architecture governance.