Autonomous Business System via Neuro-symbolic AI
#Neuro‑symbolic AI #Autonomous Business System #Large Language Models #Predicate Logic #Enterprise Knowledge Graphs #Process Automation
📌 Key Takeaways
- 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 turned into a knowledge graph and then into logic facts and rules, providing semantic grounding for reasoning.
- Large language model‑based agents synthesize task instructions, enterprise semantics, and tools into logic programs that a logic engine executes while enforcing constraints.
- Human oversight remains for defining semantics, curating tools, and handling high‑impact or ambiguous decisions.
- A case study shows accelerated time‑to‑market in a data‑rich organization, demonstrating practical benefits.
📖 Full Retelling
WHO: Researchers Cecil Pang and Hiroki Sayama.
WHAT: A novel Autonomous Business System (AUTOBUS) that blends large‑language models with predicate‑logic programming to execute end‑to‑end business initiatives.
WHERE: Released on arXiv (category cs.AI) and presented at IEEE SysCon 2026.
WHEN: Initial submission 22 Jan 2026, revised 18 Feb 2026.
WHY: To reconcile the need for continuous, cross‑functional reconfiguration in modern businesses with the deterministic, verifiable execution that traditional enterprise systems lack, by grounding AI decisions in a knowledge‑graph‑derived logic framework.
🏷️ Themes
Neuro‑symbolic AI, Business Process Automation, Enterprise Knowledge Graphs, Large Language Models, Predicate Logic Programming
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Why It Matters
The AUTOBUS system combines large language models with predicate logic to create a neuro-symbolic architecture that can execute business initiatives with verifiable constraints. This bridges the gap between natural language understanding and deterministic business process automation, potentially reducing siloed workflows and accelerating time to market.
Original Source
--> Computer Science > Artificial Intelligence arXiv:2601.15599 [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 excel at interpreting natural language and unstructured data but lack deterministic and verifiable execution of complex business logic. We introduce Autonomous Business System , 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 engine that enforces constraints and orchestrates actions. Humans define semantics and policies, curate tools, and oversee high-impact or ambiguous decisions. We present the AUTOBUS architecture and a case study that demonstrates accelerated time to market in a data-rich organization. A reference implementation of the case study is available at this https URL . Comments: IEEE SysCon 2026 Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2601.15599 [cs.AI] (or arXiv:2601.15599v2 [cs.AI] for this version) ...
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