arXiv:2606.22485v2 Announce Type: replace-cross Abstract: Decision-making in real-world settings rarely follows a fixed script. Instead, it unfolds as a dynamic reasoning process in which the appropriate course of action evolves as new context and data become available. Traditional Business Process Management systems provide rigor, determinism, and auditability, yet they generally struggle to adapt their execution at runtime. Conversely, agentic systems based on Large Language Models (LLMs) bring flexibility to decision-making, but they are inherently opaque, often unreliable, and suffer from
The proliferation of LLMs highlights the current tension between AI flexibility and the need for reliable, auditable decision-making in complex systems.
This development addresses a critical challenge in AI adoption by proposing a method to combine the adaptive power of LLMs with the predictability and control of traditional business process management.
The ability to orchestrate neurosymbolic AI workflows, allowing for adaptive yet auditable AI systems, significantly expands the practical applications of AI in enterprise and critical infrastructure.
- · AI software developers
- · Enterprise IT departments
- · Industries requiring auditable AI (e.g., finance, healthcare)
- · LLM developers
- · Systems relying solely on opaque, unreliable LLMs
Improved reliability and auditability of AI-driven decision-making processes in complex business environments.
Accelerated adoption of advanced AI agents in regulated sectors due to enhanced trust and control.
The emergence of new regulatory frameworks specifically designed for neurosymbolic AI systems.
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Read at arXiv cs.CL