Abstract: A system and method for configuration-driven continuous training of large language models in autonomous medical coding normalize multi-source electronic health data into provenance-preserving, specialty-aware schemas and provide schema-mapped context to a configurable multi-stage reasoning pipeline to generate candidate code bundles including evaluation and management levels, procedure codes, modifiers, diagnosis codes, and linkages. A ruleset-based validation layer enforces bundling, modifier applicability, and payer policies, and emits machine-readable explanations with rule identifiers, evidence references, and pass/fail indicators. A telemetry-enabled user interface captures approvals, rejections, edits, and rationales, which are consolidated with validation results into provenance-tracked training examples via a trust- or quality-weighted consensus.