![[2601.15599] Autonomous Business System via Neuro-symbolic AI](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2601.15599] Autonomous Business System via Neuro-symbolic AI
Summary
The paper presents AUTOBUS, an Autonomous Business System that integrates LLM-based AI agents with predicate-logic programming to enhance business process execution and decision-making.
Why It Matters
This research addresses the limitations of traditional enterprise systems by proposing a neuro-symbolic approach that allows for more flexible and efficient business operations. It highlights the potential of combining AI with structured logic to improve decision-making and operational agility in data-rich environments.
Key Takeaways
- AUTOBUS combines LLMs and logic programming for business processes.
- The system models initiatives as networks of tasks with clear conditions.
- It uses knowledge graphs for semantic grounding and reasoning.
- Human oversight is crucial for high-impact decisions within the system.
- A case study demonstrates improved time to market in data-rich organizations.
Computer Science > Artificial Intelligence arXiv:2601.15599 (cs) [Submitted on 22 Jan 2026 (v1), last revised 18 Feb 2026 (this version, v2)] Title:Autonomous Business System via Neuro-symbolic AI Authors:Cecil Pang, Hiroki Sayama View a PDF of the paper titled Autonomous Business System via Neuro-symbolic AI, by Cecil Pang and 1 other authors View PDF Abstract:Current business environments demand continuous reconfiguration of cross-functional processes, yet enterprise systems remain 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 and verifiable execution of complex business logic. We introduce Autonomous Business System (AUTOBUS), a system that combines LLM-based AI agents, predicate-logic programming, and business-semantics-centric enterprise data into a coherent neuro-symbolic architecture for executing end-to-end business initiatives. AUTOBUS models an initiative as a network of tasks with explicit pre- and post-conditions, required data, evaluation rules, and API-level actions. Enterprise data is represented as a knowledge graph whose entities, relationships, and constraints are translated into logic facts and foundational rules, providing semantic grounding for reasoning. Core AI agents synthesize task instructions, enterprise semantics, and available tools into task-specific logic programs executed by a logic en...