How AI Agents Accelerate Legacy System Modernization

Legacy systems hold revenue and liability. Modernization that starts with “we will rewrite it in the new stack” usually stalls in discovery: undocumented jobs, secret batch windows, and interfaces that only one person understands. AI agents help where that discovery is text, traces, and catalogs — not by hallucinating a new core.

Where agents pay for themselves

• Inventory: parse interface control documents, JCL, and API gateways into a system map.
• Contract draft: propose OpenAPI or event schemas around the strangler boundary.
• Characterization tests: generate tests from observed request/response pairs (with masking).
• Change notes: explain a 2,000-line batch in language a product owner can accept or reject.

Technical example: a read facade, not a rewrite

A first FactoryRun on a core system should almost always be read-oriented: expose a customer or order capability behind a versioned API, compare responses to the host, and only then route a channel. The agent’s job is to draft the contract and the characterization tests. The engineer’s job is to refuse invented fields.

type Characterization = {
  capability: "getOrder";
  hostExample: unknown;      // masked production trace
  facadeExample: unknown;    // new API
};

function parity(c: Characterization) {
  // Compare business keys, not JSON whitespace.
  const keys = ["orderId", "status", "amount"];
  return keys.every(
    (k) =>
      (c.hostExample as Record<string, unknown>)[k] ===
      (c.facadeExample as Record<string, unknown>)[k],
  );
}

This is the same industrial method as our legacy modernization practice: APIs, strangler fig, and a factory cadence instead of a two-year program with a big-bang weekend.