AWS Transform for mainframe now delivers a traceable reimagine workflow
Mainframe modernization now goes from COBOL assessment to traceable cloud-native code in one automated workflow — cutting years of effort to months.
View original announcement →Visual Summary
graph TD
A{{AWS Transform Reimagine Workflow}}:::announced
B((z/OS COBOL PL/I)):::external
C([Portfolio Assessment]):::feature
D([Business Rule Extraction]):::feature
E([Requirements Generation]):::feature
F([Traceable Code Generation]):::feature
G(Kiro IDE):::compute
H([MCP Integration]):::feature
I([Interactive Documentation]):::feature
B ==>|"source code"| A
A -->|"scans portfolio"| C
C -->|"selected functions"| D
D -->|"extracted rules"| E
E -->|"dev-ready reqs"| H
H -->|"flows into"| G
E -->|"traceable"| F
F -->|"cloud-native code"| G
G -.->|"generates"| I
classDef announced fill:#ff9900,stroke:#ec7211,color:#fff,font-weight:bold
classDef compute fill:#e3f2fd,stroke:#1565c0,color:#1565c0
classDef storage fill:#e8f5e9,stroke:#2e7d32,color:#2e7d32
classDef feature fill:#fff3e0,stroke:#e65100,color:#e65100
classDef external fill:#f5f5f5,stroke:#616161,color:#616161
What's New
AWS Transform for mainframe now offers a fully connected, traceable reimagine workflow that takes enterprises running z/OS COBOL and PL/I workloads from portfolio assessment all the way through cloud-native code generation in a single automated pipeline. The new capability eliminates the fragmented, multi-tool approach that previously required manual handoffs between discovery, reverse engineering, and code generation phases. Every transformation decision is fully auditable, with requirements and generated code tracing directly back to the original mainframe source.
How It Works
- Portfolio Assessment: AWS Transform systematically scans and catalogs the mainframe application portfolio, identifying and classifying discrete business functions from z/OS COBOL and PL/I codebases.
- Business Function Selection: Identified business functions are selected and flow directly into the reimagine workflow, creating a seamless, uninterrupted path from analysis to transformation without manual re-entry or tool switching.
- Business Rule Extraction: For each selected business function, the service extracts embedded business rules and logic from legacy source code using agentic AI trained on AWS migration expertise.
- Requirements Generation: AWS Transform produces development-ready requirements documents for each business function, complete with full traceability links back to the originating source code lines.
- IDE Integration via MCP: Generated requirements flow directly into Kiro (AWS's agentic IDE) and other compatible IDEs through Model Context Protocol (MCP)-based integrations, enabling developers to work within their existing toolchains.
- Interactive Documentation: Teams can generate interactive, queryable documentation for any requirement or code artifact directly inside the IDE, reducing context-switching and accelerating developer understanding.
- Traceable Code Generation: Cloud-native code is generated with an auditable chain of evidence — every requirement and every code block can be traced back to its original mainframe source, supporting compliance and review processes.
Why It's Important
- Dramatic Time Compression: What previously required years of manual analysis, reverse engineering, and re-platforming effort is compressed into months of automated, evidence-based modernization, directly reducing project cost and risk.
- Elimination of Manual Handoffs: The connected workflow removes the error-prone, time-consuming manual handoffs between discovery, analysis, and code generation phases that historically caused delays and information loss.
- Auditability and Governance: Full traceability from generated code back to original source satisfies enterprise audit, compliance, and governance requirements — a critical concern for regulated industries such as banking, insurance, and government.
- Developer Productivity: By delivering requirements and documentation directly into IDEs via MCP, developers can immediately act on transformation outputs without navigating separate portals or translating artifacts manually.
- Reduced Dependency on Mainframe Expertise: Automated extraction of business rules and generation of plain-language requirements reduces reliance on scarce COBOL/PL/I specialists, democratizing modernization across broader development teams.
- Evidence-Based Decision Making: Every transformation decision is backed by traceable evidence, giving stakeholders confidence that business logic has been correctly preserved during migration to cloud-native architectures.
How It's Different
- Single Connected Workflow vs. Multi-Tool Fragmentation: Unlike traditional modernization approaches that stitch together separate tools for discovery, reverse engineering, and code generation, AWS Transform delivers all phases in one unified pipeline with no manual handoffs.
- End-to-End Traceability: Competing tools and manual approaches rarely provide a continuous audit trail from legacy source code through requirements to generated cloud-native code; AWS Transform makes this traceability a first-class feature.
- Agentic AI at Every Phase: Rather than applying AI only at the code conversion step, AWS Transform uses agentic AI across assessment, business rule extraction, requirements generation, and code generation, compounding automation benefits throughout.
- Native IDE Integration via MCP: The use of the Model Context Protocol to push requirements directly into Kiro and other IDEs is a differentiator that keeps developers in their preferred environment rather than forcing them into a separate modernization portal.
- Portfolio-Level to Function-Level Granularity: The workflow operates at both the portfolio level (cataloging all business functions) and the individual business function level (detailed requirements and code), providing both strategic visibility and tactical execution in one tool.
- Built on 19 Years of AWS Migration Experience: The underlying AI models and automation are trained on AWS's accumulated mainframe migration domain expertise, which independent or point-solution tools cannot replicate.
When to Prefer It
- Large z/OS COBOL or PL/I Portfolios: Organizations with extensive mainframe estates where manual analysis would take years should prioritize this workflow to achieve structured, automated decomposition at scale.
- Regulated Industries Requiring Audit Trails: Enterprises in banking, insurance, healthcare, or government that must demonstrate that business logic was correctly preserved and every transformation decision is traceable to source should use this capability.
- Teams with Limited COBOL/PL/I Expertise: When the modernization team lacks deep mainframe language skills, the automated business rule extraction and plain-language requirements generation bridge the knowledge gap effectively.
- Projects Targeting Cloud-Native Architectures: When the goal is not just a lift-and-shift but a true reimagining of mainframe applications as cloud-native services, the end-to-end reimagine workflow is the appropriate path.
- Organizations Already Using Kiro or MCP-Compatible IDEs: Teams using Kiro or other IDEs with MCP support will gain the most immediate productivity benefit from requirements flowing directly into their development environment.
- Modernization Programs with Tight Timelines or Budget Constraints: When executive pressure demands compressing multi-year programs into months, the automated, connected workflow provides the acceleration needed without sacrificing quality or auditability.
- Enterprises Seeking to Reduce Vendor Lock-in on Mainframe Tools: Organizations looking to consolidate their modernization toolchain and eliminate licensing costs for multiple point solutions can simplify by adopting AWS Transform as a unified platform.
Availability
- General Availability: This traceable reimagine workflow feature is generally available as of June 16, 2026.
- Supported Regions: Available in all AWS Regions where AWS Transform for mainframe is currently supported; refer to the AWS Regional Services table for the definitive and up-to-date list.
- Supported Workloads: z/OS COBOL and PL/I mainframe workloads are explicitly supported in this release.
- IDE Integration: Integrates with Kiro (AWS's agentic IDE) and other MCP-compatible IDEs via Model Context Protocol-based integrations.
- Pricing: Specific pricing details are not disclosed in the announcement; refer to the AWS Transform for mainframe pricing page for current information.
- Limitations: No explicit limitations are stated in the announcement beyond regional availability constraints; customers should consult documentation for supported z/OS versions, codebase size limits, and any feature-level restrictions.
