CCCE: A Continuous Code Calibration Engine for Autonomous Enterprise Codebase Maintenance via Knowledge Graph Traversal and Adaptive Decision Gating

TL;DR

CCCE is an AI-driven system that autonomously maintains enterprise codebases by leveraging knowledge graphs, adaptive decision gating, and continuous learning to improve code integrity, security, and freshness across complex software environments.

Contribution

The paper introduces a novel AI-agentic system with dynamic knowledge graphs, risk-aware gating, and multi-scale learning for automated enterprise codebase maintenance.

Findings

Reduces mean time to remediation in enterprise scenarios.

Enables coordinated cross-repository calibrations with HITL oversight.

Generates atomic, verified patches with rollback and traceability.

Abstract

Enterprise software organizations face an escalating challenge in maintaining the integrity, security, and freshness of codebases that span hundreds of repositories, multiple programming languages, and thousands of interdependent packages. Existing approaches to codebase maintenance -- including static analysis, software composition analysis (SCA), and dependency management tools -- operate in isolation, address only narrow subsets of maintenance concerns, and require substantial manual intervention to propagate changes across interconnected systems. We present the Continuous Code Calibration Engine (CCCE), an event-driven, AI-agentic system that autonomously maintains enterprise codebases throughout the Software Development Life Cycle (SDLC). The CCCE introduces three key technical innovations: (1) a dynamic knowledge graph with bidirectional traversal algorithms that simultaneously compute forward impact propagation and backward test adequacy analysis; (2) an adaptive multi-stage gating framework that classifies calibration actions into four risk tiers using learned risk-confidence scoring rather than static rules; and (3) a multi-model continuous learning architecture operating at multiple temporal scales to refine calibration strategies, risk models, and organizational policies from operational feedback. We formalize the system's graph model, traversal algorithms, and decision logic, and demonstrate through three representative enterprise scenarios that the CCCE reduces mean time to remediation by enabling coordinated, cross-repository calibrations with human-in-the-loop (HITL) oversight where appropriate. The system generates atomic, semantically verified patches with progressive validation and intelligent rollback capabilities, providing end-to-end traceability from triggering events through calibration execution and outcome learning.