RPG/AS400 (IBM i) to Cloud-Native Services Automation

The bottleneck in IBM i modernization isn't just technical debt; it's the operational risk and cost of relying on a shrinking pool of RPG developers while business demands API agility. Manual translation is slow, error-prone, and fails to create a repeatable path for decomposing the monolith. A custom automation workflow directly attacks this by programmatically parsing DDS, RPG, and CL source, extracting business logic into a structured specification, and generating validated cloud-native services. This reduces migration planning from quarters to weeks and creates a controlled, auditable pipeline for incremental replacement, cutting both labor costs and transformation risk.

Implementation requires orchestrating specialized agents across parsing, graph analysis, and code generation, often using LangGraph or custom orchestrators. The workflow ingests source physical files (SPFs), uses retrieval-augmented generation (RAG) over existing documentation to resolve ambiguities, and outputs OpenAPI specs, Spring Boot services, and IaC templates. Critical controls include human review gates for complex business rules, automated test generation from legacy job streams, and a phased rollout with traffic switching logic to manage cutover risk. The architecture must integrate with enterprise CMDBs, Git repositories, and CI/CD pipelines (e.g., Jenkins, GitLab) to operationalize the generated artifacts.

This automation directly addresses the operational bottleneck of maintaining and extending IBM i applications with a shrinking talent pool. The business value comes from accelerating migration timelines by 60-70%, reducing transformation risk through automated validation, and unlocking cloud scalability for core transaction logic. The workflow ingests source physical files (PF, LF), RPGLE programs, and CL scripts to map business rules, data flows, and screen interactions into a modern service specification.

Implementation requires integrating with the IBM i via SSH or a secure gateway for source extraction. The orchestrator, built on LangGraph, manages state and handoffs between specialized parsing, generation, and validation agents. Critical controls include automated logic equivalence testing against the original green-screen transactions, exception routing for ambiguous constructs to human architects, and the generation of Infrastructure-as-Code (Terraform) for immediate deployment to Kubernetes. Observability is built into the pipeline to track translation accuracy and deployment health.

This blueprint orchestrates a controlled, incremental migration from IBM i to Kubernetes, avoiding a high-risk big-bang cutover. The workflow begins with automated discovery and inventory of RPG programs, CL scripts, and physical files, creating a system-of-record for planning. Agents then perform static analysis to map call graphs and data flows, identifying low-risk, stateless transaction modules as ideal first candidates for translation into Spring Boot or .NET Core services, which are deployed alongside the legacy system for parallel validation.

Each translated service undergoes automated unit and integration test generation based on the extracted business logic, ensuring behavioral equivalence. Deployment uses Infrastructure-as-Code templates and a canary release strategy, with traffic gradually shifted from the green-screen application to the new APIs via a routing layer. Observability agents monitor for discrepancies in transaction outcomes and data states, triggering rollback if SLA breaches occur. This phased approach reduces reliance on scarce RPG skills, validates each component before dependency cutover, and creates a repeatable pattern for the full estate.

Automated translation introduces unique governance challenges: logic must be preserved, data integrity cannot be compromised, and new services must meet performance SLAs before cutover. The workflow embeds control gates after each major phase—business logic extraction, service generation, and test validation—requiring technical and business stakeholder sign-off. This ensures the generated Kubernetes services are functionally equivalent and that any deviation from the original RPG behavior is a conscious, documented architectural decision, not an automation error.

Rollout follows a phased, traffic-based model, beginning with dark launches and synthetic transaction validation against the legacy IBM i system. A canary phase routes a small percentage of live 5250-emulated traffic to the new Kubernetes services, with automated observability comparing outcomes and performance. Any discrepancies trigger automated rollback and alert the engineering team. This controlled approach, managed by orchestration tools like Argo Rollouts or custom LangGraph agents, allows for incremental validation, minimizes user impact, and provides the operational confidence needed to complete the migration without business disruption.