Verify the specification first. Then generate.
The usual order is to generate code and check it afterwards, often with the model that wrote it. That checker shares the generator’s blind spot: whatever it misunderstood while writing, it misunderstands while reviewing. EltegraAI reverses the order. It reconstructs what your system does and why, your team approves that specification, and it is locked as the baseline before any code is generated. That order is what we mean by Autonomous Product Engineering.
Code generation got fast. Proving the new system behaves did not.
A May 2026 study asked a simple question: when a model modernizes legacy code, can the same model tell when its output has quietly changed what the program does?
modernization calls across 11 production models from 7 model families, each output tested against a strict behavioural oracle.
of attempts drifted on code with a real semantic trap, against code that needed no real change.
drift rate per model. It did not track capability or price, so the authors treat the failure as built into the task, not a matter of model size.
Articulate, and still wrong
Self-review split sharply by model: five caught every drift case they produced, one widely deployed model caught none. Several models explained the exact language distinction that broke their output, then declared the behaviour preserved.
The test corpus was legacy Python 2, a migration with public, well-known rules. Undocumented COBOL or PowerBuilder gives a model far less to go on.
Source: Reddy, Lolla and Sanku, “Articulate but Wrong: Self-Review Failures in LLM-Based Code Modernization”, arXiv 2605.21537, May 2026. Code, prompts, corpus and raw outputs are published with the paper.Someone has to sign that the new system behaves like the old one.
When a core banking, claims or billing system is rewritten, an accountable executive signs off the cut-over, and auditors ask what that signature rests on. A model’s review of its own output is not independent evidence. A specification your subject-matter experts reviewed and approved, with every generated requirement, line of code and test traced back to it, can be handed to an auditor.
That is the case for changing the order, and it is why the three tests below matter more than speed.
Autonomy is only as safe as what the system knows.
Knowledge, not retrieval — and not only from code
Most platforms retrieve context and hope it’s representative. Better ones read the source. But code tells you what a rule does, never why it exists, and the why is what breaks when you rebuild. EltegraAI reconstructs a dependency-linked graph from source, documentation, tickets, meeting transcripts and automated SME interviews with the people who still hold the knowledge — capturing why a rule exists, not just what it does.
Operational artifacts are read alongside the code, so behaviour that lives outside the code reaches the graph too.
Ask any vendor where their knowledge comes from. If the answer is only code and tickets, the intent that was never written down is not in their model.
Verified first, not reviewed after
Most platforms generate, then check. EltegraAI verifies the specification and locks it as the baseline before generation, so correctness never depends on the generator grading its own work. You correct the specification while it’s still a specification, rather than after it has become code.
The specification is built in multiple extraction and validation passes rather than a single read, and your team approves it before it is locked.
Every generated layer is built from that one locked specification, so the services and the user interface implement the same rules against the same data.
Ask any vendor when verification happens. Before or after is the whole difference.
Persistence, not project by project
One knowledge graph that compounds across every workflow, rather than re-analyzing the estate project by project. What the first modernization learns about your system is still there for the next change request, and for the application you have not started yet.
Ask any vendor what survives the engagement. If the analysis is scoped to one project, you pay for it again next time.
Committees do not buy velocity. They buy evidence — and an audit trail you can hand a regulator shortens a sales cycle faster than a demo does.
Five ways to do this. One that verifies first.
| Capability | AI coding agents | Autonomous build platforms | Behavioral modernization | Code knowledge layers | EltegraAI |
|---|---|---|---|---|---|
| Captures knowledge beyond code (docs, tickets, SMEs) | ◐ | — | ◐ | ||
| Knowledge verified and frozen before generation | — | — | ◐ | — | |
| Modernize, change and build from one foundation | ◐ | — | — | ||
| Requirement → code → test traceability | ◐ | ◐ | ◐ | — | |
| Cloud, on-premise and air-gapped deployment | ◐ | ◐ | — | ||
| Works without source code | — | — | ◐ |
Coding agents reason from syntax. When the business logic was never written down, they guess, and produce code that compiles but does not behave like the system it replaced. Those tools translate your code. EltegraAI specifies your system, then supports Claude, Cursor and Copilot through MCP. You keep the tools your engineers already use; we make them safe to point at a system that cannot afford to break.
Strongest where a system is regulated, undocumented and costly to get wrong.
Best fit
- Regulated systems in banking, insurance, healthcare, telecom and automotive, where every change needs evidence behind it.
- Undocumented systems, where the documentation drifted from the code years ago or never existed.
- High-consequence systems, where behaving differently from the old system is the failure, not a delay.
What it needs from you
- Access to the source code for modernization and change requests. Without it, the graph rests on documents, tickets and interviews alone, which is why the comparison marks it partial.
- People who still hold the knowledge. Subject-matter experts take part in automated interviews and review what the system found.
- A named approver for the specification before anything is generated.
Where it is not the right tool
- Small, well-documented applications whose existing tests already prove the rewrite behaves.
- Estates with no source code and nobody left who knows the system.
- Teams that want translation without a review step. Approving the specification is part of the product.
Three kinds of buyer, one reason to verify first
You carry the risk of the rebuild. An approved specification gives your architecture, risk and audit reviews one document to sign against, and it stays in use for every change after go-live.
Discovery is where modernization estimates go wrong. A verified specification turns the unknowns into a scoped list before you commit to a plan, inside the delivery frameworks your teams already run.
Your customers’ legacy systems are often what stalls a migration to your product. Verified knowledge of those systems, offered inside your product under licence, shortens that path.
2026 Intellyx Digital Innovator. Analyst Jason Bloomberg covered how EltegraAI turns COBOL, PowerBuilder and C++ estates into a knowledge graph that gives AI code generation deterministic, traceable context. Read the analyst brief
Eight questions to put to every vendor on your shortlist.
The “ask any vendor” lines above, gathered into one list for a buying committee. Ask each vendor to answer on your system, not on a demo.
- Where does your knowledge of our system come from?
Code alone shows what a rule does. Ask what else is read, and how intent that was never written down gets in.
- When does verification happen?
Before generation, against an approved specification, or after, by reviewing what was generated.
- Is the check independent of the generator?
If the model that wrote the code also judges it, ask for the evidence that its review catches its own errors.
- Who approves the specification, and can we correct it?
There should be a named step where your people review and sign off what the system believes, before code exists.
- Can you trace one generated line back to its rule and its test?
Pick a line at random during the evaluation and ask for the requirement and the test that cover it.
- What survives the engagement?
Ask whether the analysis is reused for the next change request or rebuilt from scratch, and paid for again.
- Where can it run?
Cloud, on-premise inside your perimeter, or air-gapped with no outbound path. Confirm which your security team needs.
- What do you need from us, and where are you not the right fit?
A vendor that cannot name its prerequisites and limits has not met your kind of system yet.
Put these questions to us, on a system like yours.
Thirty minutes with our engineering team. Describe the system you are afraid to touch, and we will answer the eight questions above for it, including where we are not the right fit. No code is needed to start, and we sign an NDA before anything technical is shared.