Authors jkordick and gkaleta share how Microsoft employs AI agents to support COBOL migration and mainframe modernization, targeting mission-critical legacy systems.

Overview

Legacy system modernization is a growing priority for organizations seeking agility and efficiency. This article, authored by jkordick and gkaleta, explores Microsoft’s strategies for tackling one of the most challenging facets—migrating mainframe-based systems written in languages like COBOL and PL/1 to cloud-native architectures.

Legacy Modernization Challenges

  • Technical Debt: Many organizations operate essential business functions on aging mainframe systems, often written in COBOL or PL/1, common in sectors like banking and insurance.
  • Complexity: Mainframes present intricate dependencies, large codebases, and unique operational paradigms.
  • Skills Gap: Fewer developers are proficient in these legacy languages, making maintenance and upgrades increasingly difficult.

Using AI Agents in Migration

Microsoft applies AI agents to accelerate and improve the modernization process. These AI-powered tools help automate code analysis, translation, and refactoring, making migration faster and reducing manual workload.

Key Capabilities of AI Agents

  • Code Comprehension: AI agents analyze complex COBOL/PL/1 codebases to identify patterns, dependencies, and business rules crucial for accurate migration.
  • Automated Conversion: These tools assist in transforming mainframe logic into modern, cloud-native architectures—such as Azure-based solutions—by generating target code or intermediate representations.
  • Intelligent Recommendations: AI agents suggest optimal migration pathways, highlight risky modules, and recommend architectural improvements for future scalability.

Benefits of Microsoft’s Approach

  • Reduced Risk: Automated tools lower migration errors and minimize downtime.
  • Faster Time-to-Cloud: Organizations can move mission-critical workloads to Azure more rapidly.
  • Continued Innovation: Modernized applications can leverage Azure’s ecosystem for analytics, AI, and scalability.

Areas of Application

  • Financial Services: Banking and insurance sectors with high dependency on mainframe workloads.
  • App Development: Extended support for cloud-native patterns and improved developer productivity.

Conclusion

Modernizing mainframe systems is a complex challenge, but the use of AI agents within Microsoft’s Azure platforms provides practical tools to accelerate migration, improve code quality, and future-proof mission-critical applications.

For more detailed insights, read the full article on All things Azure.

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