EPAM Legacy Application Modernization with AI Agents & GitHub

EPAM’s Legacy Application Modernization with AI Agents & GitHub implementation helps organizations accelerate AI Application Modernization and Legacy Code Transformation by combining automated analysis, intelligent refactoring, and scalable modernization pipelines powered by GitHub Copilot Agents and the GitHub platform. This engagement is designed for CIOs, enterprise architects, platform engineering teams, and development leaders who need to modernize large application portfolios while maintaining security, governance, and delivery speed.

Organizations operating legacy software systems often face limited documentation, tightly coupled architectures, outdated frameworks, and high technical debt. These challenges slow down development cycles and increase modernization risk. EPAM addresses these issues by implementing an AI-assisted modernization framework that uses specialized agents to analyze legacy codebases, map dependencies, generate documentation, and automate key engineering tasks.

By combining GitHub Enterprise, GitHub Actions, and GitHub Copilot Agents, the solution enables automated code analysis, accelerated framework transformation, AI-assisted testing, and modern CI/CD enablement.

What You Will Receive

• Legacy Application Portfolio Assessment: AI-powered discovery of repositories, architecture components, interfaces, and dependencies across legacy applications. Agents analyze code structures, reverse engineer data flows, detect redundant or obsolete components, and generate living documentation and knowledge packs.
• Modernization Strategy and Target Architecture: Definition of modernization pathways including replatforming, refactoring, or code conversion. Target and transition architectures are designed with security, compliance, and enterprise CI/CD practices aligned with GitHub-based development workflows.
• AI-Assisted Code Transformation: Implementation of automated Legacy Code Transformation using specialized GitHub Copilot Agents that translate frameworks, generate adapters, create unit and integration tests, and accelerate migration to modern architectures.
• Modern CI/CD Platform Enablement: Deployment of automated pipelines using GitHub Actions and enterprise development workflows including pull request automation, quality gates, security scanning, and standardized release processes.
• Modernization Factory Delivery Model: Establishment of a scalable modernization factory where applications are modernized in structured waves using reusable GitHub templates, pipelines, and AI-assisted engineering workflows.

Typical Implementation Approach

Phase 1 — Portfolio Assessment

• Inventory of legacy applications, repositories, and documentation.
• Automated dependency mapping and architecture analysis using AI agents.
• Creation of modernization knowledge packs and technical baseline documentation.

Phase 2 — Target Design and Planning

• Definition of modernization strategy, migration waves, and transition architectures.
• Design of GitHub-based development workflows and CI/CD architecture.
• Definition of testing strategies, branching models, and governance standards.

Phase 3 — AI Environment and Agent Customization

• Deployment of GitHub Copilot Agents and AI-enabled development workflows.
• Configuration of agent prompts, context engineering, and automation patterns.
• Preparation of engineering teams for AI-driven modernization practices.

Phase 4 — Modernization Pilot

• Modernization of selected pilot applications using AI-assisted engineering workflows.
• Validation of architecture, automated testing, and deployment pipelines.
• User acceptance testing and production deployment validation.

Phase 5 — Scaled Modernization

• Wave-based modernization of legacy applications using the modernization factory model.
• Automated code refactoring, framework transformation, and pipeline updates.
• Delivery of release documentation, migration scripts, and decommissioning plans.

Expected Outcomes

• Up to 30% faster dependency mapping and architecture analysis through AI-assisted discovery.
• Up to 40% higher code conversion throughput using automated transformation workflows.
• Up to 30% shorter user-story elaboration cycles enabled by AI-assisted engineering processes.
• Up to 20% faster bug resolution and improvement cycles due to AI-driven code insights.
• A scalable modernization platform powered by GitHub Enterprise, enabling future AI-native software development.