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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Agentic AI can help modernize a mainframe application by connecting natural-language change requests to application metadata, dependency analysis, code generation and verification. In IBM’s documented watsonx Code Assistant for Z 2.8 workflow, an assistant can use tools to locate affected programs, assess impact, generate code under coding standards and compile or build the result. That can speed up a developer’s work; it does not establish that a change preserves every business rule or is safe to deploy.
What “agentic” means in a mainframe modernization workflow
In this context, an agentic assistant does more than suggest a line of COBOL. It can route a request through tools that retrieve application information, analyze dependencies, generate or update code, and run a build check. The goal is to connect a developer’s intent to the application context needed to act on it.
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IBM’s 16 December 2025 announcement, updated 2 March 2026, describes this approach in watsonx Code Assistant for Z 2.8. IBM’s example request is: “I need to add a column to the Motor Policy Table that captures if the vehicle is an electric car. Can you help me update all of the programs with this field?” IBM says the workflow can retrieve metadata through Z Understand, use static-analysis-based impact analysis, generate code and apply coding standards, then compile or build to verify the change. These are vendor-described capabilities, not evidence of unattended production change management. IBM’s watsonx Code Assistant for Z 2.8 announcement describes the example and toolset.
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The useful distinction is between assistance and acceptance. An agent can help assemble context and produce a candidate change; developers still need to determine whether the affected programs are complete, the intended business meaning is correct, and test evidence is adequate.
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How the workflow moves from request to candidate change
1. Start with an application-level change request
A request such as adding an electric-vehicle indicator is more useful than asking for an isolated code snippet because the data field may be used across database definitions, COBOL programs, batch jobs, or other application artifacts. The assistant needs enough context to identify the relevant schema and consuming code rather than simply generate a plausible declaration.
2. Retrieve application metadata and map dependencies
IBM describes Z Understand as a source of application metadata for documentation and agent workflows. Its 2.8 announcement says the assistant can use metadata retrieval and static-analysis-based impact analysis to identify dependencies related to a requested change. This can reveal candidate programs and relationships for review, but teams should not treat a dependency map as proof that every implicit dependency or operational convention has been found.
3. Establish what the data and rules mean
Names and data structures rarely explain all the business semantics in a long-lived application. IBM says the 2.8 Business Rule Discovery feature is intended for large, complex Z programs, and that organization-specific data dictionaries and business glossaries can supply authoritative meanings for variables and abbreviations. Those materials can reduce guesswork only if they are current, governed and relevant to the application under change.
For example, an electric-vehicle indicator needs more than a field name: the team must settle its allowed values, source of truth, treatment of existing records, and which business processes consume it. The assistant can help explain code and surface candidate rules, but the organization must decide and validate the meaning.
4. Generate or update code within local standards
After impact analysis, code generation can produce a proposed change informed by surrounding application context and coding rules. IBM’s earlier version 2.6 announcement described natural-language chat in VS Code for creating or updating COBOL, as well as inline suggestions based on surrounding code and coding standards. The 2.8 release description presents these kinds of capabilities as part of a broader agentic workflow. Generated code remains a proposal: review its data definitions, error handling, affected call paths and compatibility with local conventions.
5. Compile or build, then review the result
A successful compile or build is useful evidence that the candidate change meets the relevant language and build constraints. It does not by itself demonstrate that every affected program behaves as intended, that integrations remain correct, or that existing business behavior is preserved. Keep the build result as one verification layer, not the acceptance decision.
Understanding code is not the same as transforming it
Code explanation helps developers understand unfamiliar logic; transformation changes that logic or its implementation. The two capabilities have different risks and should be evaluated separately.
IBM’s June 2025 version 2.6 announcement described natural-language explanations for COBOL, JCL, PL/I and REXX, with Assembler explanation introduced as a first preview. The same announcement described Business Rule Discovery and AI chat as private preview, with expected availability in late 2025. Those are release-specific historical statements: confirm current availability, supported artifacts and packaging with IBM before relying on a particular feature in a project. IBM’s version 2.6 announcement gives the release context.
Explanation can produce a readable account of what a program appears to do, but it is not proof that the account captures undocumented exceptions, external dependencies or the business intent behind a rule. Use it to accelerate investigation, then check important interpretations against source, tests, application owners and authoritative business definitions.
Choose the modernization scope: refactor, transform selectively, or retain
Mainframe modernization does not require translating every COBOL program into Java. IBM Research describes a process that models application structure and function, isolates parts for work, refactors services and supports COBOL-to-Java transformation. Its account also notes that some COBOL transaction-processing capabilities can remain useful on IBM Z. The appropriate unit of modernization is therefore a business capability or service with a clear reason to change, not an assumed whole-system rewrite.
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Refactor COBOL when the service should remain on the platform
Refactoring can disentangle a service or isolate a focused COBOL program without changing its implementation language. This can be appropriate when the team wants clearer boundaries, maintainable code or a better-defined unit for future work, while retaining existing transaction-processing strengths.
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Transform a selected service when Java is the intended target
IBM Research describes mapping COBOL data structures to Java classes and translating COBOL logic paragraph by paragraph into Java methods. That describes a transformation mechanism, not an automatic guarantee that the resulting service is equivalent, idiomatic for its target environment or ready for migration. Define the intended boundary and required interfaces before evaluating generated Java.
Keep workloads that do not need transformation where they are
A service may be a poor transformation candidate if it is tightly coupled to platform behavior, its semantics are poorly understood, or there is no business or operational reason to move it. Selectivity reduces the scope of semantic and integration risk; it does not remove the need to validate a changed or transformed component.
IBM Research’s 25 June 2025 account describes the product mechanics and IBM-reported customer examples. IBM Research’s article on watsonx Code Assistant for Z is a vendor source, not an independent product evaluation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to validate a change or COBOL-to-Java transformation
Validation should match the risk of the change. For a conventional COBOL update, compile and build checks catch some defects; application-level tests must still exercise the changed behavior and its consumers. For transformed code, equivalence testing is especially important because syntax-level success cannot show that two implementations return the same business result.
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- Build evidence: Record whether the changed code compiles and the relevant build completes. Investigate warnings and configuration differences rather than treating a green build as behavioral proof.
- Representative functional tests: Exercise normal, boundary and exception cases for the changed field or rule, including existing records and downstream uses.
- Same-input behavioral comparison: For COBOL-to-Java work, IBM Research describes running both versions with identical inputs and comparing outputs, then investigating mismatches. Select representative data and account for external calls and state that affect results.
- Integration and operational checks: Confirm that interfaces, database behavior, job flows, deployment assumptions, security controls and operational constraints remain acceptable. A unit-level match cannot establish all of these system properties.
A 2025 paper abstract by Sandeep Hans and coauthors describes symbolic-execution-based generation of COBOL unit tests, mocks for external calls, and JUnit-based checks for semantic equivalence. This is a research approach, not evidence that every generated test suite is complete or that a particular production transformation has been proven safe. The abstract for “Automated Testing of COBOL to Java Transformation” outlines that method.
Acceptance should include a human review of test coverage and the assumptions behind it. No single compile, comparison run or generated test suite proves preservation of every production integration, business outcome or security property.
What the published customer figures do—and do not—show
IBM Research reports that Egypt’s National Organization for Social Insurance (NOSI) saw a 79% reduction in the time developers needed to understand complex applications; it says some tasks fell from 24 hours to about five hours. IBM Research also reports a 60% productivity increase through COBOL-to-Java transformation at an unnamed prominent global logistics company, but does not detail how that productivity figure was measured. These are vendor-reported examples, not independently validated benchmarks or a forecast for another organization.
Questions to use when evaluating an agentic modernization workflow
Use these questions to assess a proposed workflow or product against your own application and controls; they are evaluation criteria, not a vendor ranking.
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- Application visibility: Can it retrieve metadata and expose dependencies across the programs and artifacts in scope? Can reviewers inspect how the impact set was determined?
- Artifact and language coverage: Does it handle the actual mix of COBOL, JCL, PL/I, REXX, Assembler, schemas and build artifacts required by this application? Confirm support and release status rather than inferring it from a language list in an announcement.
- Local business context: Can the team provide governed dictionaries, glossaries and other authoritative definitions? Who owns updates when a business rule changes?
- Change granularity: Can the workflow explain code, suggest or generate a bounded COBOL change, support refactoring, or transform a selected service to Java? Decide which outcome is needed before measuring success.
- Verification: Does it provide build checks, traceable test evidence and, where needed, same-input behavioral comparisons? What important risks remain outside those checks?
- Governance: Can developers review proposed changes, understand tool actions, audit outputs and follow the organization’s deployment, access and security controls?
The central test is not whether an assistant can produce code. It is whether the organization can trace a request to a justified change, inspect the evidence that the right components were touched, and demonstrate that the result meets its business and operational requirements.
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