Pre-launch · Accepting design partners

Modernize legacy code
without losing the logic inside it.

agoralb is building an AI agent that reads legacy COBOL, Java and .NET systems, surfaces the business rules buried in them, and produces modern code with tests that show it still behaves the same. Your engineers review and approve every change.

  • Built on Claude by Anthropic
  • Human-approved changes only
  • Your code is never used for training

The problem

The documentation is the code. And the people who wrote it are gone.

Rules hide in the code

Overdraft limits, rounding modes, status codes that downstream jobs depend on. Decades of decisions live in the code and nowhere else.

Rewrites stall

Manual migrations are slow and costly. Line-by-line translation tools produce code that compiles but that nobody wants to maintain.

"Equivalent" is assumed, not proven

Most migrations find behavior differences in production. The cutover is where the risk lives, so that is the part we focus on.

How it works

Four stages. A human checkpoint at each one.

An agent loop rather than a single prompt: it reads code, runs tools, executes tests, and revises its work based on what fails.

  1. 01

    Map

    Parse the repository into programs, copybooks, call graphs and data flows, so the agent works from structure instead of raw text.

  2. 02

    Extract rules

    Write every business rule in plain language, linked to the exact source lines. Domain experts confirm or correct each rule before translation starts.

  3. 03

    Translate

    Produce idiomatic code in the target stack that keeps data semantics exact: fixed-point stays fixed-point, and padded codes stay padded at the boundaries.

  4. 04

    Verify

    Generate characterization tests and run them against both the legacy and the modern code. Any difference is flagged for review and never silently "fixed".

Example

One COBOL paragraph, end to end.

A hand-prepared example of the output we are building toward. This is a static illustration, not a live run of the product.

       IDENTIFICATION DIVISION.
       PROGRAM-ID. POST-DEBIT.
       DATA DIVISION.
       WORKING-STORAGE SECTION.
       01  WS-BALANCE     PIC S9(9)V99 COMP-3.
       01  WS-AMOUNT      PIC S9(9)V99 COMP-3.
       01  WS-OVERDRAFT   PIC S9(9)V99 COMP-3 VALUE 500.00.
       01  WS-STATUS      PIC X(3).
       PROCEDURE DIVISION.
       POST-DEBIT-PARA.
           IF WS-AMOUNT > WS-BALANCE + WS-OVERDRAFT
               MOVE 'NSF' TO WS-STATUS
           ELSE
               SUBTRACT WS-AMOUNT FROM WS-BALANCE
               MOVE 'OK ' TO WS-STATUS
           END-IF.
           GOBACK.
Illustrative example · The flagged items are routed to a human reviewer before merge.

Principles

How we will handle your code.

No silent changes

Every behavior difference between the legacy and the modern code is flagged for review. The agent never "fixes" a bug on its own. Sometimes the bug is the contract.

Traceable to the line

Every rule and every generated function links back to the source lines it came from, so reviewers can audit the reasoning instead of trusting it.

Not used for training

We use Anthropic's commercial API, which does not train models on customer inputs by default. Retention terms are agreed with each partner in writing.

Engineers stay in charge

Output arrives as reviewable pull requests in your repository. Nothing merges or deploys without your team's approval.

Why Claude

The work is reading carefully, not writing quickly.

We chose Claude for the three things this problem depends on most.

Long context
A program, its copybooks and its callers fit in a single context, so rules are read where they are actually used rather than in isolated fragments.
Code reasoning
The model follows data through MOVEs, REDEFINES and implicit conversions, which is exactly where legacy semantics go wrong.
Reliable tool use
The agent calls parsers, test runners and diff tools in a loop and revises its work based on real results, not guesses.

Design partner program

Shape the product on your own codebase.

We are working with a small number of teams that have real legacy systems and a real reason to move them.

You get

  • A no-cost assessment of one module: rule inventory and migration plan
  • Direct access to the founding team
  • Preferential terms at launch

We ask for

  • A representative module (under NDA, or a sanitized copy)
  • A domain expert for about an hour a week to review rules
  • Honest feedback

FAQ

Straight answers.

Is the product available today?

Not yet. We are pre-launch and building with design partners. The example on this page is hand-prepared to show the output we are aiming for.

Isn't this just a chatbot pasting code into an LLM?

No. The model is one component. Parsing, rule tracking, test generation, and running the legacy and modern code side by side are deterministic tooling that the agent drives and checks itself against.

Which languages are in scope first?

COBOL to Java or Go, and older Java and .NET Framework to their modern versions. Partner demand decides what comes next.

Can it run on-premises?

We are designing for deployment inside your cloud account. Fully air-gapped environments are on the roadmap. Tell us your constraints in the form.

What does it cost?

Design partners pay nothing during the program. Launch pricing will be based on scope, and partners get preferential terms.

Have a system nobody wants to touch?

That is exactly the one we want to see.