Autonomous Business System via Neuro-symbolic AI
🤖 AI Summary
Enterprise systems are often hindered by departmental silos, rigid workflows, and hard-coded automation, limiting their ability to adapt flexibly to cross-functional demands. While large language models excel at processing unstructured data, they lack the capacity for deterministic execution of complex business logic. This work proposes AUTOBUS, a novel architecture that systematically applies neuro-symbolic methods to enterprise-grade business automation for the first time. By integrating large language model agents, predicate logic programming, and a business semantic knowledge graph, AUTOBUS constructs an autonomous execution system that is both verifiable and interpretable. The framework enables AI agents to generate logic programs compliant with business rules, orchestrate external tools to accomplish end-to-end tasks, and preserve human oversight in strategic definition and critical decision-making.
Application Category
📝 Abstract
Current business environments require organizations to continuously reconfigure cross-functional processes, yet enterprise systems are still organized around siloed departments, rigid workflows, and hard-coded automation. Meanwhile large language models (LLMs) excel at interpreting natural language and unstructured data but lack deterministic, verifiable execution of complex business logic. To address this gap, here we introduce AUTOBUS, an Autonomous Business System that integrates LLM-based AI agents, predicate-logic programming, and business-semantics-centric enterprise data into a coherent neuro-symbolic AI architecture for orchestrating end-to-end business initiatives. AUTOBUS models an initiative as a network of tasks with explicit pre/post conditions, required data, evaluation rules, and API-level actions. Enterprise data is organized as a knowledge graph whose entities, relationships, and constraints are translated into logic facts and foundational rules, providing the semantic grounding for task reasoning. Core AI agents synthesize task instructions, enterprise semantics, and available tools into task-specific logic programs, which are executed by a logic engine that enforces constraints, coordinates auxiliary tools, and orchestrate execution of actions and outcomes. Humans define and maintain the semantics, policies and task instructions, curate tools, and supervise high-impact or ambiguous decisions, ensuring accountability and adaptability. We detail the AUTOBUS architecture, the anatomy of the AI agent generated logic programs, and the role of humans and auxiliary tools in the lifecycle of a business initiative.
Problem
Research questions and friction points this paper is trying to address.
enterprise systems
business process reconfiguration
large language models
deterministic execution
neuro-symbolic AI
Innovation
Methods, ideas, or system contributions that make the work stand out.
Neuro-symbolic AI
Autonomous Business System
Logic Programming
Enterprise Knowledge Graph
LLM-based Agents
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