Chasing Abpms Vision: The Bab Framework Approach for LLM Agents Over Model-driven Enterprise Systems

Abstract

: Integrating Large Language Models (LLMs) into production-grade business systems requires a clear boundary between deterministic application logic and non-deterministic AI behavior. This paper introduces a runtime architecture that extends the BAB (Business Application Builder) framework on Jakarta EE to support agent-augmented applications hosted alongside ontological-driven generated cores. The core of the solution, the AgentRuntimeManager , moves beyond simple API wrappers by providing a robust environment for agent lifecycles, asynchronous event dispatching, and error handling through configurable retry policies. To ensure system stability, we decouple LLM providers (such as OpenAI and Ollama) using an abstraction layer built on LangChain4j, allowing for runtime provider switching without redeploying the core logic. We demonstrate the practical utility of this architecture through a self-healing log-monitoring chain. In this setup, agents intercept high-severity log events and, through a structured analysis service, propose corrective actions. A specialized SqlErrorHealingAgent illustrates how LLM-proposed fixes can be filtered through layered safety constraints, such as the automatic blocking of destructive SQL operations like DROP or TRUNCATE, before any action is committed. The result is an architectural pattern that lets model-driven rigor and LLM agent flexibility coexist in enterprise environments without compromising either.