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Legacy Modernization: Agents for Mainframe, ERP, and Niche Languages · AI Builds It: Easy Coding Tools

• 0:00
  SPEAKER_00: Legacy modernization with AI agents, mainframe, ERP, and niche code.
• 0:06
  SPEAKER_00: Modern enterprises often depend on decades-old software in languages like COBOL, mainframes, SAP ABAP, PLSQL, or VB6.
• 0:16
  SPEAKER_00: These aging systems are hard to change and costly to maintain.
• 0:20
  SPEAKER_00: Fortunately, new AI coding agents and design patterns now make it possible to incrementally modernize legacy stacks.
• 0:29
  SPEAKER_00: In this article, we explore how AI-driven tools help parse and rewrite old code and describe proven patterns, interface facades, the strangler approach, automated testing to replace legacy functionality gradually.
• 0:43
  SPEAKER_00: We also cover data lineage, risk controls, rollback planning, and real-world ROI versus pitfalls.
• 0:50
  SPEAKER_00: Even beginners can learn how to start.
• 0:53
  SPEAKER_00: AI now unlocks coding by turning legacy code into understandable documentation or new code, so anyone can take the first step toward modernizing an old system.
• 1:04
  SPEAKER_00: AI coding agents for legacy code.
• 1:06
  SPEAKER_00: AI coding agents are tools that use machine learning, often large language models, to read, analyze, and even rewrite code.
• 1:14
  SPEAKER_00: They can handle legacy languages no human on the team knows well.
• 1:19
  SPEAKER_00: For example, Fujitsu's new Kozuchi AI tool can analyze COBOL programs and instantly generate human readable design documents.
• 1:27
  SPEAKER_00: IBM's Watson X code assistant for Z uses AI to convert COBOL functions into high-quality Java, guiding developers through each step.
• 1:37
  SPEAKER_00: And Microsoft's open source legacy modernization agents on GitHub uses Azure OpenAI and GitHub Copilot to parse COBOL and generate equivalent Java or.NET services.
• 1:49
  SPEAKER_00: These agents capture business logic and data flows hidden in old code and help build new components around them.
• 1:55
  SPEAKER_00: The key appeal of AI agents is that anyone can start using them.
• 1:59
  SPEAKER_00: You don't need to write code by hand.
• 2:01
  SPEAKER_00: Instead, you issue prompts or use specialized tools.
• 2:05
  SPEAKER_00: For instance, a beginner could copy a small COBOL or VB6 routine into Chat GPT and ask for a plain English summary or pseudocode.
• 2:14
  SPEAKER_00: The agent understands the code structure and can propose modern equivalents.
• 2:18
  SPEAKER_00: This democratizes modernization.
• 2:20
  SPEAKER_00: Non-experts can explore legacy logic without manual code reviews.
• 2:25
  SPEAKER_00: Many vendors now bundle AI ages into accessible platforms.
• 2:29
  SPEAKER_00: CapGemini's SAP modernization solution uses generative AI to auto-document ABAP code, having effort on test scripts and conversions.
• 2:39
  SPEAKER_00: The important caveat is human oversight.
• 2:41
  SPEAKER_00: Agents speed things up, but developers still validate the output.
• 2:45
  SPEAKER_00: In sum, AI coding agents accelerate discovery and mapping of legacy systems, cutting weeks of manual analysis down to days or minutes.
• 2:54
  SPEAKER_00: Interface mapping, adapters, facades, and overlays.
• 2:58
  SPEAKER_00: One challenge of modernization is mapping interfaces between new components and the legacy core.
• 3:04
  SPEAKER_00: A common solution is an interface adapter or facade layer.
• 3:08
  SPEAKER_00: For example, ERP systems often remain as the system of record, so new UIs or services must talk to them via clean APIs.
• 3:17
  SPEAKER_00: An overlay architecture or experience layer sits between users and the old ERP.
• 3:24
  SPEAKER_00: It translates modern calls into the old system's interface and vice versa.
• 3:29
  SPEAKER_00: This adapter layer handles data mappings, authentication conversion, error handling, and buffering.
• 3:35
  SPEAKER_00: For instance, it might map legacy field names to a new domain model, Q writes when the old system is slow, and standardize error codes.
• 3:44
  SPEAKER_00: By isolating this code, you can rewrite or replace the ERP behind the facade later without changing the front end.
• 3:52
  SPEAKER_00: This pattern ensures that you can roll out improved screens and services gradually, with the adapter translating between worlds.
• 3:59
  SPEAKER_00: Another approach is to use an API gateway or facade as an entry point.
• 4:05
  SPEAKER_00: AWS illustrates this in a strangler pattern for on-prem systems.
• 4:09
  SPEAKER_00: They place an API gateway in front of the legacy app, then create new microservices behind it.
• 4:15
  SPEAKER_00: All calls go through the same API facade, whether the request is still handled by the old monolith or by a newly deployed service.
• 4:23
  SPEAKER_00: This maintains a consistent interface to customers while parts of the system strangle the old monolith.
• 4:30
  SPEAKER_00: Over time, more endpoints are rerouted to new implementations.
• 4:34
  SPEAKER_00: For example, only reading data from the old system initially, then later writing new data into the new service.
• 4:40
  SPEAKER_00: In practice, interface mapping often combines these ideas.
• 4:44
  SPEAKER_00: You deploy an adapter layer in front of the legacy system and expose a new API or web UI.
• 4:50
  SPEAKER_00: New modules call the adapter instead of talking directly to legacy database tables or screens.
• 4:56
  SPEAKER_00: This isolates old and new parts and makes it easier to redirect calls.
• 5:01
  SPEAKER_00: If a new service isn't ready yet, the adapter proxies traffic back into the legacy code.
• 5:06
  SPEAKER_00: If the new service fails, traffic can revert to the old system.
• 5:10
  SPEAKER_00: More on rollback below.
• 5:11
  SPEAKER_00: By building this shim, you can modernize one slice of functionality at a time without breaking everything.
• 5:18
  SPEAKER_00: The Strangler Fig Migration Pattern.
• 5:20
  SPEAKER_00: A related high-level pattern is the Strangler Fig approach to migration.
• 5:25
  SPEAKER_00: Coined by Martin Fowler, it compares a vine that gradually grows around a tree and eventually replaces it.
• 5:32
  SPEAKER_00: Rather than doing one big rewrite, you incrementally replace features of the old system with new ones.
• 5:38
  SPEAKER_00: Early on, you add small enhancements as separate services that run alongside or on top of the legacy code.
• 5:45
  SPEAKER_00: Over time, those new services absorb more and more business logic until the old system only handles exceptions.
• 5:52
  SPEAKER_00: New functionality and even some old features are now in the new code, and the old monolith can finally be retired.
• 5:59
  SPEAKER_00: Fowler outlines four steps for a strangler modernization.
• 6:03
  SPEAKER_00: 1.
• 6:04
  SPEAKER_00: Understand desired outcomes.
• 6:06
  SPEAKER_00: 2.
• 6:07
  SPEAKER_00: Break the problem into parts.
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  SPEAKER_00: 3.
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  SPEAKER_00: Deliver parts successfully.
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  SPEAKER_00: 4.
• 6:12
  SPEAKER_00: Change organization to sustain it.
• 6:14
  SPEAKER_00: In practice, this might mean identifying a key business capability, say order entry, rebuilding it in a new service, Node.js,.NET, etc., and then writing adapter code so calls for orders go into the new service instead of the legacy program.
• 6:30
  SPEAKER_00: Because it's done in parts, risk is reduced.
• 6:34
  SPEAKER_00: Each new piece can go live and deliver value immediately.
• 6:38
  SPEAKER_00: For example, the AWS case study had an app that first handled only simple read-only queries through the new API facade, then later added write operations for a subset of users.
• 6:50
  SPEAKER_00: At every step, the system kept working for users.
• 6:53
  SPEAKER_00: AI coding agents help with Strangler migrations by quickly creating or refactoring those new components.
• 7:00
  SPEAKER_00: For instance, an agent can read legacy COBOL logic about calculating employee bonuses and generate an equivalent Java or Python function.
• 7:09
  SPEAKER_00: You then deploy that as a service under the Strangler pattern.
• 7:13
  SPEAKER_00: A key to success is building transitional interfaces.
• 7:16
  SPEAKER_00: Code that exists only until migration is done.
• 7:20
  SPEAKER_00: Many teams balk at extra waste code to connect old and new, but this transitional logic, routing, data syncing, etc., is what makes gradual migration feasible with lower risk.
• 7:31
  SPEAKER_00: Automated test harness for legacy code.
• 7:34
  SPEAKER_00: One lesson from failed migrations is that unrecognized errors can cripple a rewrite.
• 7:39
  SPEAKER_00: To modernize safely, you need a comprehensive automated test harness around the legacy system.
• 7:45
  SPEAKER_00: In practice, this means writing tests at multiple levels and integrating them into a build pipeline.
• 7:52
  SPEAKER_00: Unit tests, verify individual functions or modules.
• 7:55
  SPEAKER_00: In legacy code, business logic may be hidden in large routines.
• 7:59
  SPEAKER_00: Agents can help by suggesting unit tests.
• 8:02
  SPEAKER_00: For example, asking an AI agent to propose input-output examples for a legacy function.
• 8:08
  SPEAKER_00: Tools and frameworks, e.g., modern COBOL or PL SQL test runners, can execute legacy code against these tests.
• 8:16
  SPEAKER_00: Integration tests.
• 8:18
  SPEAKER_00: Check that modules interact correctly.
• 8:20
  SPEAKER_00: For instance, if your new overlay writes to an ERP database, an integration test ensures the end-to-end flow, entry, and UI to update an ERP still works.
• 8:31
  SPEAKER_00: Agents may assist by auto-generating requests based on interpretation of interface definitions.
• 8:37
  SPEAKER_00: End-to-end E2E tests.
• 8:39
  SPEAKER_00: Simulate full user workflows.
• 8:41
  SPEAKER_00: Before migration, you establish golden sequences of operations.
• 8:45
  SPEAKER_00: Login, create an invoice, etc.
• 8:48
  SPEAKER_00: Crawlers or frameworks, like Cypress Playwright, can automate GUI or API calls for those flows.
• 8:54
  SPEAKER_00: This is crucial.
• 8:55
  SPEAKER_00: It catches issues no unit test can.
• 8:58
  SPEAKER_00: Regression tests, the safety net.
• 9:00
  SPEAKER_00: Every time you refactor or cut over a feature, run your entire suite to ensure nothing else broke.
• 9:07
  SPEAKER_00: Characterization tests, a classic legacy technique, are especially helpful.
• 9:12
  SPEAKER_00: They record current outputs of legacy code for given inputs and assert that the new code matches that behavior.
• 9:19
  SPEAKER_00: In other words, tests capture what the code actually does, so you don't need to know why it does it.
• 9:26
  SPEAKER_00: Experts emphasize that regression testing is the most important layer.
• 9:30
  SPEAKER_00: Before any change, make sure you have tests covering core functionality.
• 9:35
  SPEAKER_00: Start by protecting mission critical flows, orders, billing, approvals, anything directly tied to revenue or compliance.
• 9:43
  SPEAKER_00: Then expand tests to fragile or high change areas, modules with many past bugs.
• 9:48
  SPEAKER_00: You don't need to do it all at once.
• 9:50
  SPEAKER_00: Build your suite iteratively.
• 9:52
  SPEAKER_00: For example, when a tester finds a bug, write a new test around that scenario.
• 9:57
  SPEAKER_00: Over months of consistent effort, even a skeleton suite can grow enough to catch major regressions.
• 10:04
  SPEAKER_00: AI can automate aspects of testing too.
• 10:07
  SPEAKER_00: For instance, AI test platforms, like some CICD tools, can generate intent-based end-to-end tests from natural language specifications.
• 10:16
  SPEAKER_00: An agent can scan legacy code and documentation, then suggest test cases.
• 10:21
  SPEAKER_00: In SAP modernization, Cap Gemini's tools promise to automate test script generation with 40% effort reduction.
• 10:29
  SPEAKER_00: And the native industry analysis found that writing tests still often takes 40 to 50% of a legacy project, but AI can cut that dramatically.
• 10:38
  SPEAKER_00: Conceptually, you could feed a COBOL job log or legacy UI flow into an LLM to get a sample sequence of actions for testing.
• 10:45
  SPEAKER_00: Regardless, the human must validate AI suggestions.
• 10:49
  SPEAKER_00: The goal is confidence that new code matches old behavior before reintegration.
• 10:54
  SPEAKER_00: Data lineage and risk controls.
• 10:56
  SPEAKER_00: Legacy modernization isn't just about code.
• 10:59
  SPEAKER_00: Data must move or stay consistent too.
• 11:02
  SPEAKER_00: Data lineage means tracking where every data element came from and how it's transformed.
• 11:07
  SPEAKER_00: Without clear lineage, it's almost impossible to ensure the migrated system is accurate and compliant.
• 11:13
  SPEAKER_00: For example, when mainframe data, often in EBCDIC format, is moved to a modern platform, enterprises require forensic hash mapping and chain of custody processes.
• 11:24
  SPEAKER_00: In practice, this means computing cryptographic hashes of data at each stage so you can prove it wasn't altered.
• 11:31
  SPEAKER_00: It also means logging every ETL step, every extract, transform, or load, is auditable.
• 11:37
  SPEAKER_00: Without this, auditors or regulators might not trust your new system.
• 11:40
  SPEAKER_00: Data quality is a huge risk area.
• 11:43
  SPEAKER_00: A modern guide warns that most failed legacy data migrations weren't due to technology, but due to dirty data copied straight over.
• 11:50
  SPEAKER_00: Duplicate records, silent field drops, or inconsistent formats that crept into the old system can poison the new one, if not addressed.
• 11:58
  SPEAKER_00: It's essential to perform data profiling and cleansing before migration, not just rely on the ETL tool to move bytes.
• 12:07
  SPEAKER_00: Teams should ask, have we identified duplicate customer records and decided how to merge them?
• 12:14
  SPEAKER_00: Will every important field, even rarely used ones, map to the new schema?
• 12:19
  SPEAKER_00: Is there a clear rollback plan if we later discover migration errors?
• 12:23
  SPEAKER_00: Risk controls start with validating data at every step.
• 12:28
  SPEAKER_00: Migrate in controlled batches.
• 12:30
  SPEAKER_00: For instance, move history for five years of transactions first.
• 12:34
  SPEAKER_00: Check reports for accuracy, then proceed with the rest.
• 12:38
  SPEAKER_00: Use reconciliation scripts.
• 12:40
  SPEAKER_00: After each batch, verify row counts and checksums match.
• 12:44
  SPEAKER_00: If discrepancies appear, pause and clean data rather than forging ahead.
• 12:50
  SPEAKER_00: Maintain a backup or transactional log of source data so you can revert any failed batch without rerunning the entire migration.
• 12:58
  SPEAKER_00: In high-stakes cases, you might even run source and target in parallel for a while.
• 13:04
  SPEAKER_00: Dual write, so all new updates go to both systems until the new one is fully confirmed.
• 13:09
  SPEAKER_00: Essentially, build guardrails like you would in production, monitoring, alerts, and quick rollback triggers.
• 13:16
  SPEAKER_00: Rollback strategies.
• 13:18
  SPEAKER_00: Despite careful planning, migrations can encounter problems.
• 13:21
  SPEAKER_00: A clear rollback strategy is non-negotiable to limit impact.
• 13:26
  SPEAKER_00: The exact approach depends on your risk tolerance and downtime window.
• 13:31
  SPEAKER_00: Here are common options.
• 13:32
  SPEAKER_00: Fail-safe replication.
• 13:34
  SPEAKER_00: Keep the old database in sync with the new system.
• 13:37
  SPEAKER_00: For example, use Change Data Capture in both directions.
• 13:42
  SPEAKER_00: After cutover, continue replicating from the new system back to the old.
• 13:46
  SPEAKER_00: If things go wrong, you can instantly restart the old system with no lost rights.
• 13:52
  SPEAKER_00: This is used in cloud migrations, e.g.
• 13:54
  SPEAKER_00: AWS DMS, Cockroach DB failback.
• 13:58
  SPEAKER_00: Dual write or parallel run.
• 14:00
  SPEAKER_00: Modify the application code or use integration middleware to write every transaction to both the legacy and new systems during a trial period.
• 14:09
  SPEAKER_00: Then, if the new system fails, simply redirect clients back to the legacy environment.
• 14:14
  SPEAKER_00: Dual write means no new data is lost on rollback, but it does double the write overhead and complexity.
• 14:21
  SPEAKER_00: Manual cutover plus snapshot.
• 14:23
  SPEAKER_00: For very low-risk cases, take a final snapshot of the legacy database.
• 14:28
  SPEAKER_00: Switch users to the new system and rely on manual data reconciliation if issues appear.
• 14:34
  SPEAKER_00: This is only acceptable if you can tolerate some potential inconsistencies and have time to fix them.
• 14:40
  SPEAKER_00: Feature flags, partial switchover.
• 14:43
  SPEAKER_00: In a strangler approach, control what goes to new versus old via configuration.
• 14:48
  SPEAKER_00: If a problem arises in a new component, you can toggle it off, routing requests back to the legacy with no code rollback.
• 14:55
  SPEAKER_00: This is like a very fine-grained rollback at the API level.
• 14:59
  SPEAKER_00: Regardless of method, define rollback criteria and run books ahead of time.
• 15:04
  SPEAKER_00: For example, if error rate spikes above X or critical data fails checks, initiate rollback steps.
• 15:12
  SPEAKER_00: A recent review emphasizes matching rollback complexity to your needs.
• 15:16
  SPEAKER_00: If zero data loss is critical, implement bidirectional replication or dual write.
• 15:22
  SPEAKER_00: If some minor loss is tolerable, then manual fallback might suffice.
• 15:27
  SPEAKER_00: Importantly, test your rollback procedures before the big cutover so the team knows how to execute them under pressure.
• 15:34
  SPEAKER_00: ROI of modernization.
• 15:36
  SPEAKER_00: It's natural to worry about the cost of modernization.
• 15:40
  SPEAKER_00: However, real-world cases show that the ROI can be very high.
• 15:45
  SPEAKER_00: Legacy systems often consume 60 to 80% of an IT budget just for maintenance of alt code.
• 15:51
  SPEAKER_00: Compared to that ongoing drag, a one-time upgrade can pay back quickly.
• 15:56
  SPEAKER_00: Industry analysis suggests AI-assisted modernization can cut project costs by around 70 to 80%.
• 16:04
  SPEAKER_00: For instance, converting a 50,000-line app manually might cost$240,000.
• 16:10
  SPEAKER_00: With AI tools, it could drop to$57,000, about a 76% reduction.
• 16:16
  SPEAKER_00: That calculation includes labor, quality assurance, and tool fees.
• 16:20
  SPEAKER_00: In practice, many firms report five-year ROIs of 200 to 400%, often breaking even in one to two years.
• 16:28
  SPEAKER_00: Concrete success stories abound.
• 16:30
  SPEAKER_00: Deloitte describes a U.S.
• 16:32
  SPEAKER_00: state that avoided a$200 million, 10-year rewrite of a COBOL child support system by using automated refactoring into Java on the cloud.
• 16:41
  SPEAKER_00: They completed it in 18 months instead, freeing budget for modern services.
• 16:46
  SPEAKER_00: A Dutch insurer, NN Group, converted over 10 million COBOL lines to Java and cut IT platform costs by 80%, recouping investment in under three years.
• 16:57
  SPEAKER_00: Even on smaller scales, AI helpers can accelerate discovery and coding.
• 17:02
  SPEAKER_00: One benchmark cited a legacy migration going from 8 to 11 months down to about two months with agents, with labor costs falling by about$183,000 for a 50,000 line code base.
• 17:15
  SPEAKER_00: Of course, ROI depends on factors like continuing maintenance savings, reduced downtime, and the opportunity cost of new features.
• 17:23
  SPEAKER_00: By automating the grunt work, AI agents free up skilled developers to build new products rather than babysit old systems.
• 17:30
  SPEAKER_00: They also mitigate talent risk.
• 17:32
  SPEAKER_00: Fewer firms need to scramble for COBOL or VB6 experts if AI can handle the legacy logic.
• 17:39
  SPEAKER_00: All told, organizations find full stack modernization more affordable and faster than ever, especially when done incrementally.
• 17:47
  SPEAKER_00: Pitfalls and lessons learned.
• 17:49
  SPEAKER_00: While AI and patterns bring advantages, there are cautionary points.
• 17:53
  SPEAKER_00: First, AI hallucinations and errors are real.
• 17:56
  SPEAKER_00: Generative tools can invent code or documentation that looks plausible but is incorrect.
• 18:01
  SPEAKER_00: Fujitsu's solution addresses this by using a proprietary knowledge graph overlay that reduces hallucinations when generating design docs.
• 18:10
  SPEAKER_00: Always validate AI output against known references or sample runs.
• 18:15
  SPEAKER_00: Second, testing remains a bottleneck.
• 18:18
  SPEAKER_00: Even if code conversion is fast, testing often still takes 40 to 50% of the schedule.
• 18:25
  SPEAKER_00: Many teams underestimate this.
• 18:27
  SPEAKER_00: You must devote time to robust CI pipelines and possibly AI-assisted test generation.
• 18:34
  SPEAKER_00: Do not cut corners on test coverage.
• 18:36
  SPEAKER_00: Legacy code is brittle by nature, and inadequate tests are a common cause of failure.
• 18:42
  SPEAKER_00: Third, data issues often derail projects.
• 18:45
  SPEAKER_00: As noted, technical migration success is meaningless if the data quality is poor.
• 18:50
  SPEAKER_00: Failing to profile and cleanse data led many migrations to generate a new broken system.
• 18:56
  SPEAKER_00: Invest in a data checklist, deduplicate, map every field, and include business stakeholders to define what clean data means.
• 19:05
  SPEAKER_00: Build reconciliation reports before going live so you catch mistakes early.
• 19:10
  SPEAKER_00: Fourth, scope creep, and feature mismatch can surprise teams.
• 19:14
  SPEAKER_00: Legacy systems often have hidden business logic and hacks embedded.
• 19:19
  SPEAKER_00: Do not assume the old system's behavior is fully understood.
• 19:22
  SPEAKER_00: Use characterization tests, as described earlier, to capture current behavior and involve domain experts to explain unusual cases.
• 19:31
  SPEAKER_00: When migrating UI or APIs, plan for the fallback where the old interface remains until the new one is proven equivalent.
• 19:39
  SPEAKER_00: Finally, people and process change matters.
• 19:42
  SPEAKER_00: Patterns like the Strangler require organizational buy-in.
• 19:46
  SPEAKER_00: Teams must adopt new agile practices or team structures to let old and new coexist during transition.
• 19:52
  SPEAKER_00: Getting business units to accept phased rollouts and testers to learn new tools is as important as the code.
• 19:59
  SPEAKER_00: As Fowler notes, without cultural change, the new system could end up as muddled as the old one.
• 20:05
  SPEAKER_00: Getting started.
• 20:06
  SPEAKER_00: First steps.
• 20:07
  SPEAKER_00: For readers eager to try AI modernization on their own, here's a practical way to begin.
• 20:13
  SPEAKER_00: Inventory a small module.
• 20:15
  SPEAKER_00: Pick a contained functionality, e.g., a single COBOL program, ABAP function group, or VB6Forb.
• 20:22
  SPEAKER_00: Gather its source code and any sample inputs.
• 20:25
  SPEAKER_00: Let AI explain it.
• 20:27
  SPEAKER_00: Use a tool like ChatGPT or an AI code assistant.
• 20:30
  SPEAKER_00: Paste the code or key excerpts and ask for a summary or pseudocode.
• 20:35
  SPEAKER_00: For example, explain the business logic of this COBOL code.
• 20:39
  SPEAKER_00: The agent will highlight loops, calculations, and data usage in plain language.
• 20:44
  SPEAKER_00: This bridges human understanding with the legacy syntax.
• 20:48
  SPEAKER_00: Generate a test or doc.
• 20:50
  SPEAKER_00: Prompt the agent to produce a test case for that code.
• 20:53
  SPEAKER_00: Or ask it to output a diagram or API schema of what that module does.
• 20:58
  SPEAKER_00: You may get an initial unit test or design doc for free.
• 21:02
  SPEAKER_00: Build a harness.
• 21:03
  SPEAKER_00: Even a simple script that calls the old code with test inputs and checks outputs establishes a baseline.
• 21:10
  SPEAKER_00: If the agent provided outputs, verify they match the actual program.
• 21:14
  SPEAKER_00: This check also trains you to spot AI errors.
• 21:17
  SPEAKER_00: Decide how this functionality will live in the new architecture.
• 21:22
  SPEAKER_00: Will it become a REST microservice?
• 21:24
  SPEAKER_00: A cloud function?
• 21:26
  SPEAKER_00: Sketch the data contracts.
• 21:28
  SPEAKER_00: You can ask the agent, convert this legacy output into JSON fields.
• 21:32
  SPEAKER_00: Use a sample migration tool.
• 21:35
  SPEAKER_00: For example, Microsoft's Legacy Modernization Agents repo includes demo agents for COBOL.
• 21:41
  SPEAKER_00: Or try a trial of a tool like Phoenix Code, which supports Delphi, Power Builder, VB6, etc., to see automated conversions for your language.
• 21:50
  SPEAKER_00: Involve your team.
• 21:52
  SPEAKER_00: Share the AI outputs with colleagues or business analysts.
• 21:56
  SPEAKER_00: Validate with a domain expert.
• 21:58
  SPEAKER_00: Is this translation correct?
• 22:00
  SPEAKER_00: Keep iterating.
• 22:01
  SPEAKER_00: The first next step is simply experimentation.
• 22:04
  SPEAKER_00: Pick a non-critical piece of legacy code and run it through an AI tool.
• 22:08
  SPEAKER_00: Play with prompts until you get a meaningful conversion or explanation.
• 22:12
  SPEAKER_00: This low-stakes experiment gives insight into both the promise and quirks of these agents.
• 22:18
  SPEAKER_00: From there, you can expand to a formal strangler phase, define the first feature to strangle, and write the adapter code needed.
• 22:26
  SPEAKER_00: Conclusion: Modernizing legacy systems no longer means reading 40-year-old COBOL by flashlight or hiring scarce experts.
• 22:34
  SPEAKER_00: AI coding agents and smart architecture patterns have opened the door for even novices to make progress.
• 22:40
  SPEAKER_00: By using incremental methods, API facades overlays, and strangler migration, building strong automated tests, including characterization tests, and planning data validation and rollback, organizations can transform old stacks safely.
• 22:56
  SPEAKER_00: The ROI can be dramatic as studies show costs having or more.
• 23:00
  SPEAKER_00: The key is to stay disciplined, validate AI outputs, involve business users to define correctness, and don't skip the plumbing like tests and logging.
• 23:10
  SPEAKER_00: Start small, iterate, and learn from each slice you modernize.
• 23:14
  SPEAKER_00: With these tools and practices, that 30-year-old system can evolve into something nimble and future ready, and the next person up can link your new modernized system together with confidence.
• 23:24
  SPEAKER_00: All links to sources are available in the text version of this article.
• 23:28
  SPEAKER_00: You can find the full article at easycoding.toolslash blog.
• 23:32
  SPEAKER_00: Thanks for listening.
• 23:34
  SPEAKER_00: For more practical AI coding guides, tool comparisons, and resources for builders, visit easycoding.tools.
• 23:41
  SPEAKER_00: And if it's easier to remember, just go to aibuilds.it.
• 23:45
  SPEAKER_00: It redirects to easycoding.tools.