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AI-Driven Refactoring and Coding in ASP.NET Core

Architectural Guide for Senior .NET Architects

Sampath Dissanayake · January 6, 2026

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Executive Summary

This guide explores how AI-Driven Refactoring and Coding in ASP.NET Core accelerates modernization for enterprise .NET teams.

AI-driven refactoring is reshaping ASP.NET Core development by automating code analysis, dependency mapping, and modernization patterns—positioning senior .NET architects for high-compensation roles across cloud-native enterprise platforms.

Research reports 40–60% productivity gains through AI-assisted AST parsing and context-aware transformations—critical for migrating legacy monoliths into scalable microservices running on Azure PaaS.
This guide explores cutting-edge tools including Augment Code, JetBrains ReSharper AI, and agentic IDE platforms (Antigravity) used in production modernization programs.

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Deep Dive

Internal Mechanics of AI Code Analysis

AI refactoring engines parse Abstract Syntax Trees (ASTs) to construct dependency graphs across file boundaries, tracking:

• Method calls

• Import chains

• Variable scopes

• Cross-project coupling

Unlike naive regex search/replace, AI models understand Razor markup, MVC controllers, Minimal API endpoints, middleware pipelines, and DI lifetimes simultaneously—preserving routing and container wiring.

Modern platforms employ multi-agent orchestration:

• Agent #1: Static analysis

• Agent #2: Transformation planning

• Agent #3: Validation + automated test execution

This aligns with Azure DevOps pipelines that also scaffold C# 12+ primitives including primary constructors, collection expressions, and record-based response models.

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Architectural Patterns Identified

Research identifies three dominant AI-driven refactoring patterns:

1. Monolith Decomposition

AI detects tightly coupled components and identifies separation boundaries aligned with Domain-Driven Design (DDD) aggregates.

2. API Modernization

Automatic conversion of MVC controllers to Minimal APIs with:

• Fluent route mapping

• Input validation

• Swagger/OpenAPI generation

3. Performance Refactoring

AI detects:

• N+1 queries

• Misuse of ToList()

• Sync-over-async patterns

• Memory inefficiencies

Recommendations include spans, batching, IQueryable filters, and async enforcement.

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Technical Implementation

Below is an example of AI-refactored ASP.NET Core controller logic using modern C# 12 features.

Before AI Refactoring (Inefficient)

After AI Refactoring (Optimized)

AI tools automatically enforce:

• Minimal API handlers

• Primary constructor injection

• Span-based memory optimizations

• Immutable records
  …while preserving business logic integrity.

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Real-World Scenario

In a financial enterprise processing 10M+ transactions daily:

• AI decomposes monolithic ExpenseService into DDD contexts: Approval, Audit, Reporting

• Controllers convert to Minimal APIs hosted via Azure Container Apps with Dapr

• Deployment pipeline:

  • GitHub Actions → AI code review → Azure DevOps → AKS

• Result:

  • 73% faster deployments

  • 40% memory reduction

  • Improved scaling predictability

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Performance & Scalability Considerations

• Memory — AI flags stack allocations >85KB and recommends spans

• Database — Eliminates ToList().Where() misuse in favor of IQueryable

• Scaling — Generates manifests with HPA rules based on custom metrics

• Cold Starts — Enforces ReadyToRun and tiered compilation

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Decision Matrix

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Expert Insights

Pitfall: Context window limits — AI stalls past 10k LOC
Fix: Chunk code by bounded context

Optimization Trick:
Let AI handle 80% mechanical churn, humans handle 20% architecture

Undocumented Insight:
Semantic fingerprinting prevents regressions across branches

Azure Hack:
Pipe AI changes through Logic Apps to generate PRs with before/after benchmarks

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Conclusion

AI-driven refactoring marks a new architectural era for ASP.NET Core—from tactical cleanup to strategic modernization.
By 2027, 75% of enterprise .NET workloads will leverage agentic development platforms, making AI modernization proficiency table stakes for principal architect roles.
Microsoft’s Semantic Kernel, ML.NET, and Azure-native ecosystems position .NET as the epicenter of this shift.

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FAQs

How does AI preserve dependency injection?
By analyzing Roslyn semantic models & DI registrations.

Best tools for monolith decomposition?
Augment Code, ReSharper AI, Antigravity, .NET Aspire observability.

Can AI introduce performance regressions?
Yes—block PRs unless BenchmarkDotNet regression <5%.

CI/CD integration?
AI → Git patch → Azure DevOps YAML → SonarQube → Auto-merge gate.

What C# 12 features get introduced?
Primary constructors, spans, collection expressions, trimming compatibility.

Cost?
ReSharper AI: ~$700/yr, Augment Code: ~$50/dev/mo. ROI in 2–3 months.

Headless Architecture in .NET Microservices with gRPC

AI-Driven .NET Development in 2026: How Senior Architects Master .NET 10 for Elite Performance Tuning

.NET Core Microservices and Azure Kubernetes Service

.NET Core Microservices on Azure

✔️ AI + .NET Development

Microsoft Learn – AI-assisted development for .NET
https://learn.microsoft.com/dotnet/ai/

✔️ ASP.NET Core Architecture

ASP.NET Core Fundamentals
https://learn.microsoft.com/aspnet/core/fundamentals/

✔️ Dependency Injection Deep Dive

Dependency Injection in ASP.NET Core
https://learn.microsoft.com/aspnet/core/fundamentals/dependency-injection