Dependency First AI Overviews for IBM i and Laravel Modernization

  • September 7, 2026
  • Ty Woods
  • 14 min read

Optimize AI overviews by running a dependency-first investigation, mapping the repository, tracing real capabilities, extracting business rules, before any refactor plan gets written. Every inferred rule needs evidence and a human triage decision attached to it. Some tools build this discipline into their workflows. Done right, you get fast AI drafts that your team can actually trust and act on.

TL;DR:

  • Effective AI overviews require dependency mapping, tracing real capabilities, and extracting evidence-backed business rules before planning refactors.
  • Hybrid workflows with human validation and confidence labels outperform pure automation, especially for modules with high risks like payroll or billing.
  • Grounding retrieval with task-specific context, composing diagrams bottom-up, and running characterization tests help mitigate AI hallucinations and unreliable inferences.
  • Proper handling of AI model updates involves tagging outputs with model versions, re-baselining tests, and maintaining rollback options to prevent inconsistencies.
  • Running AI tools on proprietary code demands strict access controls, data handling policies, and compliance measures for sensitive environments.

Table of Contents

Why AI Overviews Optimization Matters for Legacy Systems

An AI overview of a legacy system is not a summary paragraph. It is a package: a dependency map showing which modules call which, extracted business rules with citations back to specific files, sequence diagrams for critical capabilities, and a list of open questions the AI could not resolve on its own. Get that package right and a modernization team can scope a Laravel upgrade or an IBM i RPG rewrite in weeks instead of months. Get it wrong, and you inherit confident nonsense baked into a migration plan.

The failure modes are predictable. Large language models hallucinate function behavior when they cannot see the full call chain. They miss cross-file callers because most tools feed them one file at a time with no memory of the rest of the repository. They flatten implicit contracts, the unwritten assumptions a 20-year-old RPG program makes about the state of a file or the order operations run in, into confident, wrong prose. Research on LLM-based refactoring shows this pattern clearly: language models improve structural code metrics, cleaner formatting, reduced complexity scores, while sometimes destabilizing the actual runtime behavior when semantic or inter-module dependencies come into play.

That gap is exactly why hybrid workflows outperform pure automation:

  • AI drafts the first pass fast, often in hours instead of weeks
  • Humans validate every inferred business rule before it enters a modernization plan
  • Confidence labels separate what the AI confirmed from what it guessed
  • Traceability links tie every claim back to a specific file and line

The core tradeoff: agentic pipelines pay off when you’re documenting hundreds of modules with moderate individual risk. Manual investigation still wins on the ten modules that touch payroll, billing, or regulatory reporting, where a wrong inference costs far more than the time saved.

How Do You Run a Dependency-First Investigation?

The workflow that produces trustworthy AI overviews follows a fixed sequence, and skipping steps is where most modernization teams get burned. A repository-first investigation approach keeps AI in investigation mode rather than letting it jump straight to a rewrite it has no basis for recommending.

  1. Map the repository. Identify entry points, list every module, and catalog the tech stack in play, RPG, CL, embedded SQL, PHP, whatever the estate actually contains. Every claim the AI makes later must link back to a file in this map.
  2. Choose one real capability and trace it end-to-end. Pick something concrete, “Create Order” or “Process Payroll Adjustment”, and follow it from entry point to database write. Document every input, every output, and every side effect along the way, including the ones nobody remembers writing down.
  3. Extract candidate business rules with evidence. Each rule gets a citation to the file and line it came from, and a confidence label: confirmed, inferred, or gap.
  4. Build lightweight dependency maps. Module-level maps show which programs call which. Data-flow maps show which fields move where. Every edge on the diagram cites the source file that justifies it.
  5. Record unknowns explicitly. Anything the AI could not verify becomes a task on a punch list, not a footnote buried in prose.

The order matters more than the individual steps. Skip the trace step and jump straight to rule extraction, and you get plausible-sounding business logic that nobody actually wrote into the code. Skip the unknowns step, and those gaps resurface as production incidents six months into a Laravel Ascend upgrade instead of as a checklist item during discovery.

What separates a useful overview from a dangerous one is what happens with the “unknown” pile. Teams that treat every unresolved question as a verification task before refactoring catch the landmines. Teams that let the AI paper over gaps with fluent, confident language inherit those landmines during cutover.

What Tooling Patterns Actually Reduce Hallucination?

Retrieval-augmented generation matters, but only if someone checks what it retrieves. Industry analysis of AI coding agents on legacy codebases makes the point directly: RAG reduces hallucination by grounding the model in real code, but poor retrieval quality produces plausible-sounding APIs that simply do not exist. Audit what your retrieval layer pulls back before trusting the analysis built on top of it.

A few practices separate teams that get reliable overviews from teams that get expensive rewrites:

  • Task-scoped context files. Give the AI a narrow, purpose-built context for each investigation task instead of dumping the entire codebase into a single prompt. Teams that invest in this kind of context engineering see their agent failure rates drop across repeated runs, not just the first one.
  • Bottom-up diagram composition. Generate a diagram for each file, then merge them at the call sites rather than asking one model call to reason about the whole system at once. Toyota Motor Europe’s mainframe documentation project used exactly this pattern with Amazon Bedrock, processing files independently and composing results hierarchically to handle arbitrary call depth without blowing out token budgets.
  • Characterization tests before any change. Generate them from observed behavior, have a human review them, then run them against the current code so you have a working contract before anyone touches a line.
  • Confidence-and-gap tracking. A structured gaps.md file beats fluent, overconfident prose every time a stakeholder asks “how sure are we about this?”

Pro Tip: Run the same retrieval query against three different chunking strategies before committing to one. If the top result changes depending on how you split the files, your RAG layer is guessing, not retrieving.

Cost and latency matter too. Prompt caching, selective RAG instead of full-repository retrieval, and per-file processing keep a documentation pipeline affordable at the scale most enterprise IBM i or PHP estates require.

How Should Teams Validate AI-Generated Overviews Before Acting?

Every extracted rule needs a human decision attached to it, not just a confidence score. The Reversa framework for reverse documentation engineering separates discovery, writing, and review into distinct roles specifically to prevent one fragile inference from silently becoming a fact by the time it reaches a modernization plan.

The triage gate is where that separation earns its keep. Someone with domain knowledge classifies each extracted business rule as intended (the system is supposed to work this way), a tolerated bug (wrong, but downstream systems depend on the wrong behavior now), or obsolete (dead code from a process that no longer exists). Record the decision and the reasoning, not just the label.

Characterization tests function as the enforceable contract on top of that triage. Generate them from observed behavior, have a human check them against the trace from your investigation phase, run them against the existing system to confirm they pass, then use that same suite to verify any refactor did not change behavior it should have preserved.

A few governance habits keep this from decaying after the initial push.

  • Store documentation as code inside the repository, versioned alongside the software it describes
  • Wire artifact regeneration into CI so dependency maps and business-rule extracts get flagged the moment source files drift
  • Require human architectural sign-off before any change that crosses service boundaries or alters a dependency edge
  • Publish an artifact only after its evidence links resolve to real source files and its triage decision is recorded

Practitioner guidance on documentation-as-code treats characterization tests as documentation in their own right, arguably the most reliable verification mechanism available for agent-assisted refactoring, because they check behavior rather than trusting a narrative description of it.

A Checklist to Start Your First Pilot This Quarter

Pick your pilot modules using one rule: prioritize modules with high business impact, frequent change requests, and low existing test coverage. That combination is where AI-assisted investigation pays off fastest, because it is where tribal knowledge is thinnest and the cost of a wrong guess is highest.

Minimum artifacts to publish per module, each gated by acceptance criteria before it counts as done:

  1. A dependency map citing source files for every edge
  2. A business-rule extract with confidence labels (confirmed, inferred, gap)
  3. A characterization test suite, human-reviewed and passing against current behavior
  4. An open-questions log converted into verification tasks

Sample prompts worth adapting for your own pilot:

  • “Inspect this repository’s structure and list every entry point, module, and external dependency.”
  • “Trace the ‘Create Order’ capability end-to-end. Document every input, output, and side effect.”
  • “List every observable side effect of this function, including file writes, external calls, and shared state.”
  • “Build a module-level dependency map for this directory, citing the source file for each connection.”

If the pilot surfaces dependencies deeper than your team can trace confidently within two weeks, or the codebase spans thousands of RPG or Laravel modules with no existing documentation, that is the signal to bring in a firm like Golden Path Digital’s IBM i modernization assessment rather than stretching internal capacity thin.

Strategies for Managing AI Model Updates Without Breaking Overview Consistency

Model updates are the quiet risk nobody plans for. A vendor ships a new version of the underlying model behind your documentation pipeline, and suddenly the confidence labels shift, the phrasing changes, or a rule that was flagged “inferred” last month gets flagged “confirmed” this month for no reason tied to the actual code.

Treat every model version as a distinct artifact source, not an invisible upgrade. Tag each generated overview with the model version that produced it, the same way you’d tag a build with a compiler version. When you swap models, rerun the characterization test suite against a sample of previously documented modules and compare outputs side by side before trusting the new version’s extracts wholesale.

Version drift compounds fastest in confidence labeling. A model update that shifts how aggressively the system labels inferences as “confirmed” is more dangerous than one that changes prose style, because it directly affects what your triage gate treats as settled. Re-baseline your gap logs whenever the underlying model changes, and don’t assume a newer model is strictly more accurate on your specific codebase just because it scores higher on general benchmarks.

Keep a rollback path. If a model update degrades output quality on your test set of known-good modules, the pipeline should let you pin the previous version until the regression gets sorted out. Consistency across a multi-year modernization program matters more than always running the newest release.

Strategies for Managing AI Model Updates Without Breaking Overview Consistency: overview diagram

Security Considerations When Running AI Tools on Proprietary Code

Your RPG source, your Laravel application logic, and your business rules are not generic training data, and they should never leave your control without a clear data-handling agreement in place. Before running any AI tool against a proprietary codebase, confirm where the code goes: does the vendor process it in a private instance, does it get retained for model training, and what’s the deletion policy after analysis completes?

Access control matters as much as data handling. Not every engineer needs the same visibility into an AI-generated overview of a payroll or claims-processing module. Scope access to dependency maps and business-rule extracts the same way you’d scope access to the underlying source, by role, by module sensitivity, and by need. A documentation pipeline that exposes every extracted business rule to every contractor on a project defeats the purpose of having access controls on the source code in the first place.

For regulated industries, HIPAA compliance and similar frameworks extend to AI-generated derivatives of protected systems, as well as the original source. If your codebase touches healthcare data, financial records, or other regulated information, the overview artifacts themselves need the same handling discipline as the code they describe. Audit logs, encrypted storage, and documented data flows apply to the dependency map just as much as to the database it describes.

How We Approach Dependency-First AI Overviews

Some tools apply exactly this sequence: map first, extract second, refactor last. Others apply a similar evidence chain to framework upgrades and workflow automation once modernization work hands off downstream. When a pilot surfaces dependency chains your team cannot trace confidently within a sprint or two, that’s the signal to bring in external help rather than stretch internal capacity.

Ty

Get a Dependency-First Modernization Assessment

Specialized services exist for exactly the moment described above: when your IBM i RPG estate or Laravel application has grown too tangled to document by hand, and you need dependency maps and business-rule extracts you can actually stake a migration plan on.

Golden Path Digital

Our approach centers on legacy code modernization services built around the same investigation-first sequence covered above: map the repository, trace real capabilities, extract rules with evidence, and gate everything through human triage before a single line changes. AS/Forward handles the parsing and dependency mapping for IBM i RPG codebases. Laravel Ascend automates the mechanical parts of version upgrades from Laravel 6 through 11 once the dependency picture is clear. QuantaPath AI picks up workflow and CRM automation downstream, with compliance controls built in for regulated environments.

A scoped pilot typically delivers a dependency map, a business-rule extract with confidence labels, and a prioritized punch list for your highest-risk modules, work you can review before committing to a full engagement. Ready to see what your codebase actually contains? Request a modernization assessment and get a scoped view of what a pilot would cover.

Sources

FAQ

What Is an AI-Generated Codebase Overview?

It’s a structured package, a dependency map, business-rule extracts with source citations, and an open-questions log, produced by running AI analysis against a codebase rather than a single narrative summary.

How Do You Prevent AI Hallucination in Dependency Mapping?

Audit retrieval quality directly, since poor retrieval produces confident but nonexistent APIs, and require every mapped dependency to cite the specific file and line that justifies it.

Should We Refactor Based on AI-Extracted Business Rules Alone?

No. Every inferred rule needs human triage, classified as intended, a tolerated bug, or obsolete, before it informs a refactor, and research on LLM-based refactoring confirms hybrid human-validated workflows outperform pure automation on behavioral accuracy.

What Happens When the Underlying AI Model Changes Mid-Project?

Confidence labels and phrasing can shift with a new model version, so re-baseline your characterization tests and gap logs against a sample of previously documented modules before trusting new outputs.

Can Golden Path Digital Run a Pilot on Our Codebase?

Some tools apply the dependency-first investigation workflow described here, and a scoped pilot can typically produce a dependency map and business-rule extract within weeks.