How We Modernized a 20-Year-Old .NET Platform—And How Gen AI Could Do It Even Faster

A few years ago, I led the modernization of a 20-year-old .NET enterprise platform riddled with over 1,200 stored procedures, monolithic dependencies, and on-premises infrastructure. The system, though critical to the business, had become a bottleneck—fraught with downtime, spiraling costs, and an inability to innovate. Today, as ?? cloud and ?? AI redefine what’s possible, I’ll share our journey, the strategies that worked, and how Generative AI (Gen AI) can now automate what once took us months to untangle.

I will also propose an Agentic AI–based migration strategy that can 10X reduce the time needed for modernization.

The Problem: A Legacy Anchored in Time

The platform exhibited classic symptoms of aging systems:

? Outdated Stack: .NET Framework 3X, SQL Server 2008, and manual deployments.

? Stored Procedure Spaghetti: Business logic buried in SQL scripts, with no documentation. ? Rigid Infrastructure: On-premises servers unable to scale for seasonal workloads.

? Innovation Gridlock: No CI/CD, no cloud integration, and zero AI capabilities.

?? Our goal was not just to “move to the cloud” but to rebuild the system as a future-proof, AI-ready asset.

Strategic Approaches: Balancing Risk and Ambition

We adopted a phased strategy to minimize disruption while maximizing long-term value.

Assessment & Prioritization

• ?? Automated Inventory Analysis: Used tools like Azure Migrate to scan the codebase, while custom scripts mapped stored procedure dependencies.
• ?? Gen AI-Powered Risk Scoring: AI models flagged procedures with compliance risks (e.g., GDPR violations) or performance bottlenecks.
• ?? Quick Wins: Migrated static reporting modules to Azure App Service first, demonstrating early ROI.

Migration Pathways

• Rehost (for non-critical batch jobs): AI-generated pre/post-migration checklists streamlined processes.
• Refactor (business logic in stored procedures): Converted T-SQL to C# using GitHub Copilot, accelerating work by 20-40%.
• Rearchitect (customer-facing modules): AI-assisted dependency analysis helped define microservice boundaries.
• Replace (legacy ETL workflows): Low-code pipelines using Azure Logic Apps modernized data flows.

Stored Procedure Modernization: Where Gen AI Shined

Phase 1??: Decoding the “Black Box”

?? Key Tools:

• SQL Server Data Tools (SSDT): Helped visualize dependencies between stored procedures.
• Azure OpenAI: Generated plain-English summaries of undocumented SQL logic.

Example:

• Legacy: sp_CalculateRevenue
• AI Summary: “Aggregates sales data by region, applies tax rules from a deprecated 2003 policy table.”

?? Gen AI Use Case: Extracted user stories from stored procedures using a RAG model with GPT-4.

Phase 2??: Refactoring at Scale

?? Key Tools:

• Azure OpenAI: Assisted in code transformation.
• ApexSQL Refactor: Standardized SQL formatting and error handling.

Gen AI Use Case: Automated 30% of T-SQL-to-C# translations, accelerating modernization efforts.

Phase 3??: Optimization & Cloud Shift

?? Key Tools:

• Azure SQL Managed Instance: Hosted procedures with minimal rework.
• Azure Synapse Analytics: Offloaded reporting to reduce load on transactional databases.

Gen AI Use Case: AI-driven execution plan analysis helped optimize queries, reducing run times by 35%.

Enter Agentic AI: A Step Beyond Generative AI

While Generative AI (Gen AI) accelerates modernization through code translation, dependency analysis, and optimization, Agentic AI takes modernization to the next level. It autonomously orchestrates workflows, makes real-time decisions, and continuously optimizes the system, reducing human intervention and enabling rapid transformation.

Powering Modernization with LangGraph

LangGraph is a powerful framework for building Agentic AI workflows, ensuring seamless migration and intelligent automation. It structures modernization with autonomous AI nodes that interact seamlessly. Below are some potential Agents defined as Nodes in LangGraph.

1?? Discovery Node: AI scans the legacy system, identifying dependencies and risks using LLM-powered analysis.

2?? Translation Node: Uses retrieval-augmented generation (RAG) to convert stored procedures into API-based services.

3?? Optimization Node: Conducts AI-based performance analysis and recommends optimizations.

4?? Deployment Node: Automates CI/CD pipelines, ensuring continuous integration and delivery.

5?? Monitoring Node: Deploys self-healing AI agents for proactive anomaly detection and issue resolution.

Bridging the Old and the New

Modern Tech Stack

1. Legacy: .NET Framework 3.5, SQL Server 2008, manual deployments, on-prem VM clusters.
2. Modernized: .NET 8.0, Azure Functions, Azure SQL DB, Cosmos DB, Kubernetes (AKS), and CI/CD pipelines with Azure DevOps.
3. AI-Driven Enhancements: GitHub Copilot (code generation), Azure OpenAI (SQL-to-C# translation), predictive scaling with Azure ML.

Measuring Success

? Stored Procedure Reduction: 65% replaced by APIs or serverless functions.

? Deployment Speed: Transitioned from quarterly releases to weekly deployments via AI-assisted testing.

? Cost Efficiency: Achieved a 40% reduction in TCO through Azure reserved instances.

? AI Adoption: Successfully integrated AI-driven chatbots and predictive maintenance systems.

?? Key Metrics Tracked:

• Technical Debt Score: Evaluated using SonarQube and AI-powered risk models.
• Mean Time to Repair (MTTR): Reduced by 50% through AI-driven root cause analysis.
• Business Agility: Post-migration surveys rated system flexibility at 4.5/5.

Lessons Learned

?? Decouple Before Migrating: Extracting stored procedures into APIs was challenging but essential for long-term scalability.

?? Start Small, Automate Early: Automating on non-critical modules built confidence across teams.

?? AI is a Force Multiplier, Not a Silver Bullet: While AI accelerated automation, human oversight remained crucial.

Conclusion

Modernizing a legacy .NET platform is as much about cultural transformation as it is about technology upgrades. By leveraging Generative AI to automate code conversion, requirement extraction, and optimization, we cut project timelines by 25% and turned a legacy liability into a cloud-native asset.

Today, this once-outdated system powers real-time analytics, AI-driven customer experiences, and seamless scalability—proving that even the oldest systems can evolve with the right strategy.

?? The future belongs to those who modernize boldly.