COBOL and AI: Building a Self-Serve Knowledge Layer for 2,000 Batch Jobs

Modernization planning stalls when the business rules are locked inside decades of COBOL code. This talk shares a practical, production‑tested playbook I used to extract those rules, make them explainable, and serve them to teams in a usable form. It’s not economical to have humans extract this level of operational knowledge from COBOL at scale. The outcome of this work is an agent that saves hours for operational staff by surfacing what a batch job does, which input files it consumes, and which outputs it produces.

I’ll walk through the end‑to‑end pipeline: how we used AI to parse COBOL into control‑ and data‑flow structures, generating diagrams that make execution paths and data dependencies visible, and assembling structured knowledge about each job (purpose, inputs, outputs, key rules). The emphasis is on trade‑offs: what we automated vs. where we needed human review, which COBOL constructs are most error‑prone, and how we scaled the approach across a legacy estate of ~2,000 COBOL jobs. Converting specific modules to Python is shown as one possible downstream outcome—but the core goal is understanding and planning. I will demo a self‑serve knowledge agent we built for developers and business analysts. It makes available the original code repositories plus the derived diagrams and extracted rules, so teams can ask questions like “where is premium eligibility calculated?” and get grounded answers with traceable sources. This will include a live demo using a public COBOL repository so the workflow is reproducible without proprietary code.