Building AI-Powered Architecture Review Systems with .NET

Introduction

Architecture reviews play a critical role in modern software development. They help teams evaluate system design decisions, identify scalability concerns, enforce engineering standards, and reduce technical risks before applications reach production. However, as organizations grow and engineering teams become larger, conducting consistent and thorough architecture reviews becomes increasingly difficult.

Senior architects often spend significant time reviewing design documents, evaluating system diagrams, validating technology choices, and ensuring compliance with organizational standards. This process can become a bottleneck, especially in fast-moving development environments.

Artificial Intelligence offers a new approach. By combining Large Language Models (LLMs), enterprise architecture knowledge, engineering standards, and review workflows, organizations can build AI-powered architecture review systems that assist developers and architects throughout the software development lifecycle.

In this article, we'll explore how to build AI-powered architecture review systems using ASP.NET Core, Azure OpenAI, Azure AI Search, and Semantic Kernel.

What Is an AI-Powered Architecture Review System?

An AI-powered architecture review system is an intelligent assistant that evaluates software architecture designs against predefined standards, best practices, and organizational requirements.

Instead of manually reviewing every design document, teams can submit architecture proposals and receive automated feedback.

Typical capabilities include:

Architecture analysis
Design pattern recommendations
Security reviews
Scalability assessments
Cloud architecture validation
Compliance checks
Technical debt identification

Example questions include:

Is this microservices design scalable?
Are there any security concerns in this architecture?
Does this design follow our engineering standards?
What risks exist in this deployment strategy?

The AI system acts as an architectural advisor rather than replacing human architects.

Why Organizations Need AI Architecture Reviews

Many engineering organizations face common challenges.

Limited Architect Availability

Senior architects cannot review every project in detail.

Inconsistent Reviews

Different reviewers may apply different standards.

Growing System Complexity

Cloud-native architectures introduce numerous design decisions.

Faster Delivery Expectations

Teams need rapid feedback during development.

AI-powered review systems help address these challenges by providing consistent and scalable guidance.

Core Architecture

A typical architecture review solution includes:

Developer
     ↓
Architecture Submission
     ↓
ASP.NET Core API
     ↓
Review Engine
     ↓
Knowledge Retrieval
     ↓
Azure OpenAI
     ↓
Review Recommendations

This architecture combines enterprise knowledge with AI reasoning capabilities.

Knowledge Sources

The quality of the review system depends on the quality of its knowledge.

Common sources include:

Architecture Standards

Engineering guidelines
Design principles
Coding standards

Cloud Best Practices

Azure architecture patterns
Security frameworks
Scalability recommendations

Historical Reviews

Previous architecture decisions
Approved designs
Lessons learned

Internal Documentation

Platform standards
Infrastructure requirements
Operational policies

The richer the knowledge base, the more valuable the review process becomes.

Building the ASP.NET Core Backend

ASP.NET Core serves as the orchestration layer.

Example endpoint:

[HttpPost("review")]
public async Task<IActionResult> Review(
    ArchitectureRequest request)
{
    var result =
        await _reviewService
            .AnalyzeAsync(request);

    return Ok(result);
}

The API receives architecture information and returns AI-generated recommendations.

Integrating Azure OpenAI

Azure OpenAI provides the reasoning capabilities needed for architecture analysis.

Example setup:

var client =
    new AzureOpenAIClient(
        endpoint,
        credential);

The model evaluates architectural decisions and generates review feedback.

However, enterprise context remains essential.

Using Retrieval-Augmented Generation

Architecture reviews should be grounded in organizational standards.

Workflow:

Architecture Proposal
         ↓
Knowledge Retrieval
         ↓
Relevant Standards
         ↓
LLM Analysis
         ↓
Recommendations

Example prompt:

var prompt = $"""
Review the architecture
using the following standards:

{standards}

Architecture:
{proposal}
""";

This approach improves consistency and accuracy.

Integrating Azure AI Search

Azure AI Search enables retrieval of architecture-related content.

Examples include:

Architecture guidelines
Security standards
Design patterns
Previous review decisions

Search example:

var documents =
    await searchClient.SearchAsync(
        architectureTopic);

Retrieved content becomes part of the review process.

Using Semantic Kernel for Workflow Orchestration

Semantic Kernel can coordinate multiple review activities.

Install:

dotnet add package Microsoft.SemanticKernel

Configuration:

var builder = Kernel.CreateBuilder();

builder.AddAzureOpenAIChatCompletion(
    deploymentName: "gpt-4",
    endpoint: endpoint,
    apiKey: apiKey);

var kernel = builder.Build();

The kernel can orchestrate multiple review stages and tool interactions.

Example Review Categories

A mature review system typically evaluates several areas.

Scalability

Questions include:

Can the system handle growth?
Are bottlenecks identified?

Security

Evaluate:

Authentication
Authorization
Data protection
Network security

Reliability

Review:

Fault tolerance
Disaster recovery
Redundancy

Maintainability

Analyze:

Service boundaries
Modularity
Technical debt

Cost Efficiency

Assess:

Resource utilization
Infrastructure choices
Cloud spending implications

These categories mirror many traditional architecture review processes.

Example Architecture Analysis

Developer submits:

Microservices architecture with
10 services communicating
synchronously through REST APIs.

Potential AI feedback:

Consider asynchronous communication for resilience.
Introduce message queues for high-volume workloads.
Evaluate service dependency risks.
Review API gateway requirements.

The system provides actionable recommendations rather than simple pass/fail results.

Automating Architecture Checklists

Organizations often maintain review checklists.

Example:

Category	Review Question
Security	Is authentication implemented?
Scalability	Can services scale independently?
Reliability	Are failure scenarios handled?
Monitoring	Is observability included?

AI systems can automatically evaluate these criteria.

Architecture Risk Scoring

Review systems can assign risk levels.

Example:

Security Risk: Medium

Scalability Risk: Low

Operational Risk: High

Risk scoring helps prioritize review findings.

Integrating Historical Decisions

One powerful capability is learning from previous architecture reviews.

Examples:

Approved patterns
Rejected approaches
Historical incidents
Technical debt findings

The AI can reference organizational experience when providing recommendations.

Monitoring Review Quality

Organizations should track:

Review accuracy
Recommendation adoption
User satisfaction
False positives
Review completion time

Example:

_logger.LogInformation(
    "Architecture review completed");

Metrics help improve system effectiveness over time.

Security Considerations

Architecture documents often contain sensitive information.

Recommended controls include:

Authentication

Require verified users.

Access Control

Restrict access to architecture artifacts.

Data Protection

Encrypt stored review data.

Audit Logging

Track review activity.

These controls support enterprise security requirements.

Best Practices

Start with Existing Standards

Use current architecture guidelines as the initial knowledge base.

Keep Human Oversight

AI should augment architects, not replace them.

Continuously Update Knowledge

Review criteria should evolve alongside technology changes.

Track Review Outcomes

Measure the effectiveness of recommendations.

Focus on Explainability

Provide reasoning behind recommendations.

These practices improve adoption and trust.

Common Challenges

Organizations frequently encounter:

Incomplete architecture documentation
Evolving standards
Complex system dependencies
Recommendation quality issues
Knowledge maintenance requirements

Addressing these challenges early improves long-term success.

Future of AI Architecture Reviews

Emerging capabilities include:

Automated architecture diagrams analysis
Architecture compliance monitoring
Continuous architecture validation
Agent-based design reviews
Real-time architecture recommendations

These innovations will help teams build more reliable systems.

Conclusion

AI-powered architecture review systems provide a scalable way to improve software design quality, enforce engineering standards, and accelerate architectural decision-making. By combining ASP.NET Core, Azure OpenAI, Azure AI Search, and Semantic Kernel, organizations can build intelligent review platforms that assist developers and architects throughout the development lifecycle.

Rather than replacing architectural expertise, these systems enhance it by providing faster feedback, consistent evaluations, and broader access to organizational knowledge. For .NET developers, architecture review assistants represent a practical and high-impact application of enterprise AI that can significantly improve software quality and engineering efficiency.