AWS re:Invent 2025 - Automating enterprise-scale code modernization with agentic AI (MAM342)

Automating Enterprise-Scale Code Modernization with Agentic AI

Overview

This presentation from AWS re:Invent 2025 explores how organizations can leverage agentic AI to automate the complex process of modernizing legacy applications at scale. The speaker delves into the key challenges of enterprise-level code modernization and how the latest advancements in artificial intelligence can help overcome these obstacles.

The Challenge of Enterprise-Scale Code Modernization

• Modernizing legacy applications is a critical but highly complex and resource-intensive process for large organizations.
• Key challenges include:
  • Vast codebase size and complexity
  • Lack of documentation and institutional knowledge
  • Diverse technology stacks and architectural patterns
  • Tight integration with mission-critical business systems
  • Compliance and regulatory requirements
  • Maintaining business continuity during the modernization effort

Agentic AI for Automated Code Modernization

• The presentation introduces the concept of "agentic AI" - AI systems that can autonomously analyze, understand, and transform complex codebases.
• These AI agents leverage advanced natural language processing, deep learning, and reinforcement learning to:
  • Comprehend the structure, dependencies, and business logic of legacy applications
  • Identify modernization opportunities and optimal migration paths
  • Automatically refactor and migrate code to modern cloud-native architectures
  • Validate the correctness and performance of the modernized applications
• Key capabilities of the agentic AI system include:
  • Automated code analysis and understanding
  • Intelligent code transformation and refactoring
  • Continuous testing and validation
  • Seamless integration with DevOps toolchains

Enterprise-Scale Deployment and Governance

• The presentation discusses how organizations can deploy and govern agentic AI systems at scale to modernize their entire application portfolio.
• Strategies include:
  • Centralized control plane for managing multiple AI agents
  • Federated learning to share knowledge and best practices across agents
  • Robust governance and compliance frameworks to ensure security and regulatory adherence
  • Seamless integration with existing DevOps pipelines and tooling

Real-World Case Studies and Results

• The speaker presents several case studies of large enterprises that have successfully leveraged agentic AI for their code modernization initiatives:
  • A global financial institution that modernized 2,000+ legacy applications in 18 months, achieving 30% cost savings and a 50% reduction in technical debt.
  • A multinational retailer that migrated its entire e-commerce platform to a cloud-native architecture, improving performance by 40% and reducing operational costs by 25%.
  • A government agency that automated the modernization of its mission-critical legacy systems, ensuring compliance with new security and data regulations while minimizing service disruptions.

Key Takeaways

• Agentic AI represents a transformative approach to enterprise-scale code modernization, enabling organizations to overcome the inherent challenges of legacy application transformation.
• By leveraging advanced AI capabilities, organizations can automate the complex tasks of code analysis, refactoring, and validation, accelerating modernization efforts and reducing the associated risks and costs.
• Successful deployment of agentic AI for code modernization requires a holistic approach that includes centralized governance, federated learning, and seamless integration with existing DevOps workflows.
• The case studies presented demonstrate the significant business impact that can be achieved through the application of agentic AI, including cost savings, technical debt reduction, and improved application performance and reliability.