AWS re:Invent 2025 - 4x faster workload modernization with agentic AI (MAM349)

AWS re:Invent 2025 - 4x faster workload modernization with agentic AI (MAM349)

Overview

This presentation from AWS re:Invent 2025 focuses on a new approach to accelerating workload modernization using agentic AI. The key highlights include:

• Introducing a novel "agentic AI" framework that enables 4x faster modernization of legacy workloads
• Demonstrating how this AI-powered approach can automate and streamline the entire modernization lifecycle
• Showcasing real-world customer case studies and the significant business impact achieved

Agentic AI for Workload Modernization

• Agentic AI Concept: The presenters introduce the concept of "agentic AI" - an AI system that can autonomously navigate the modernization process, make decisions, and take actions on behalf of human users.
• Modernization Lifecycle Automation: This agentic AI framework is designed to automate the entire workload modernization lifecycle, including:
  • Workload assessment and migration planning
  • Automated code refactoring and containerization
  • Seamless deployment to modern cloud architectures
  • Ongoing monitoring and optimization

Technical Capabilities

• Intelligent Workload Analysis: The agentic AI system leverages advanced machine learning models to analyze legacy workloads, identify modernization opportunities, and generate detailed migration plans.
• Automated Refactoring: The system can automatically refactor legacy code, containerize applications, and optimize them for cloud-native deployment - reducing manual effort by up to 80%.
• Continuous Optimization: The agentic AI continuously monitors the modernized workloads, identifies performance bottlenecks, and autonomously makes adjustments to maintain optimal efficiency.

Customer Case Studies

• Financial Services Firm: A large financial services company was able to modernize 150 legacy applications in just 6 months, achieving a 40% reduction in infrastructure costs and a 35% improvement in application performance.
• Retail Manufacturer: A global retail manufacturer used the agentic AI system to modernize its entire supply chain management platform, resulting in a 45% decrease in time-to-market for new product launches and a 25% increase in operational efficiency.
• Public Sector Agency: A government agency leveraged the agentic AI to modernize its citizen-facing web applications, leading to a 50% improvement in user satisfaction and a 30% reduction in IT support tickets.

Key Takeaways

• The agentic AI framework can significantly accelerate the workload modernization process, reducing the time and effort required by up to 4x.
• Automated analysis, refactoring, and optimization capabilities enable organizations to modernize legacy systems more efficiently and with greater agility.
• Real-world customer case studies demonstrate the substantial business benefits, including cost savings, performance improvements, and enhanced user experiences.
• This AI-powered approach to workload modernization represents a transformative shift in how organizations can leverage cloud technologies to drive digital transformation.